{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 1. KMeans"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1.1 简单例子"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.cluster import KMeans\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = np.array([[1, 2], [1, 4], [1, 0],\n",
    "              [10, 2], [10, 4], [10, 0]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "kmeans = KMeans(n_clusters=2, random_state=0).fit(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 1, 1, 0, 0, 0])"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "kmeans.labels_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 0])"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "kmeans.predict([[0, 0], [12, 3]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[10.,  2.],\n",
       "       [ 1.,  2.]])"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "kmeans.cluster_centers_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1.2 多种情况效果展示"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "from sklearn.cluster import KMeans\n",
    "from sklearn.datasets import make_blobs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "n_samples = 1500\n",
    "random_state = 170\n",
    "X, y = make_blobs(n_samples=n_samples, random_state=random_state)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "函数原型：\n",
    "- sklearn.datasets.make_blobs(n_samples=100, n_features=2, centers=None, cluster_std=1.0, center_box=(-10.0, 10.0), shuffle=True, random_state=None)\n",
    "\n",
    "参数解释：\n",
    "- n_samples(int/array):如果参数为int，代表总样本数；如果参数为array-like，数组中的每个数代表每一簇的样本数。  \n",
    "- n_features(int):样本点的维度。  \n",
    "+ centers(int):样本中心数。如果样本数为int且centers=None，生成3个样本中心；如果样本数（n_samples）为数组，则centers 要么为None，要么为数组的长度。  \n",
    "- cluster_std(float/sequence of floats):样本中，簇的标准差。\n",
    "- center_box(pair of floats (min, max)):每个簇的上下限。\n",
    "- shuffle(boolean):是否将样本打乱。\n",
    "- random_state(int/RandomState instance /None):指定随机数种子，每个种子生成的序列相同，与minecraft地图种子同理。\n",
    "\n",
    "返回类型：\n",
    "- X : 样本数组 [n_samples, n_features]:产生的样本\n",
    "- y : array of shape [n_samples]:每个簇的标签"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1.2.1 给定错误聚类个数"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred = KMeans(n_clusters=2, random_state=random_state).fit_predict(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5,1,'Incorrect Number of Blobs')"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(X[:, 0], X[:, 1], c=y_pred)\n",
    "plt.title(\"Incorrect Number of Blobs\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1.2.2 各向异性分布的数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 旋转数据\n",
    "transformation = [[0.60834549, -0.63667341], [-0.40887718, 0.85253229]]\n",
    "X_aniso = np.dot(X, transformation)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred = KMeans(n_clusters=3, random_state=random_state).fit_predict(X_aniso)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5,1,'Anisotropicly Distributed Blobs')"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(X_aniso[:, 0], X_aniso[:, 1], c=y_pred)\n",
    "plt.title(\"Anisotropicly Distributed Blobs\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1.2.3 每簇的方差不同"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_varied, y_varied = make_blobs(n_samples=n_samples,\n",
    "                                cluster_std=[1.0, 2.5, 0.5],\n",
    "                                random_state=random_state)\n",
    "y_pred = KMeans(n_clusters=3, random_state=random_state).fit_predict(X_varied)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5,1,'Unequal Variance')"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(X_varied[:, 0], X_varied[:, 1], c=y_pred)\n",
    "plt.title(\"Unequal Variance\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1.2.4 簇的大小分布不均衡"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_filtered = np.vstack((X[y == 0][:500], X[y == 1][:100], X[y == 2][:10]))\n",
    "y_pred = KMeans(n_clusters=3,\n",
    "                random_state=random_state).fit_predict(X_filtered)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5,1,'Unevenly Sized Blobs')"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(X_filtered[:, 0], X_filtered[:, 1], c=y_pred)\n",
    "plt.title(\"Unevenly Sized Blobs\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 2. 层次聚类之凝聚聚类"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2.1 简单例子"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.cluster import AgglomerativeClustering\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = np.array([[1, 2], [1, 4], [1, 0],\n",
    "              [4, 2], [4, 4], [4, 0]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "AgglomerativeClustering(affinity='euclidean', compute_full_tree='auto',\n",
       "            connectivity=None, linkage='ward', memory=None, n_clusters=2,\n",
       "            pooling_func=<function mean at 0x00000218641FF158>)"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clustering = AgglomerativeClustering().fit(X)\n",
    "clustering"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 1, 1, 0, 0, 0], dtype=int64)"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clustering.labels_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2.2 聚类层次树-dendrogram"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from scipy.spatial.distance import pdist,squareform\n",
    "from scipy.cluster.hierarchy import linkage\n",
    "from scipy.cluster.hierarchy import dendrogram\n",
    "from sklearn.cluster import AgglomerativeClustering"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "             X         Y         Z\n",
      "ID_0  1.915195  6.221088  4.377277\n",
      "ID_1  7.853586  7.799758  2.725926\n",
      "ID_2  2.764643  8.018722  9.581394\n",
      "ID_3  8.759326  3.578173  5.009951\n",
      "ID_4  6.834629  7.127020  3.702508\n"
     ]
    }
   ],
   "source": [
    "# 随机生成数据\n",
    "np.random.seed(1234)\n",
    "variables = ['X','Y','Z']\n",
    "labels=['ID_0','ID_1','ID_2','ID_3','ID_4']\n",
    "X=np.random.random_sample([5,3])*10\n",
    "df = pd.DataFrame(X,columns=variables,index=labels)\n",
    "print (df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "           row label1  row label2  distance  no. of items in clust.\n",
      "cluster 1         1.0         4.0  1.563509                     2.0\n",
      "cluster 2         3.0         5.0  4.884559                     3.0\n",
      "cluster 3         0.0         2.0  5.570985                     2.0\n",
      "cluster 4         6.0         7.0  8.749445                     5.0\n"
     ]
    }
   ],
   "source": [
    "row_clusters = linkage(pdist(df,metric='euclidean'),method='complete')\n",
    "print(pd.DataFrame(row_clusters,\n",
    "                   columns=['row label1','row label2','distance','no. of items in clust.'],\n",
    "                   index=['cluster %d'%(i+1) for i in range(row_clusters.shape[0])]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "row_dendr = dendrogram(row_clusters,labels=labels)\n",
    "plt.tight_layout()\n",
    "plt.ylabel('Euclidean distance')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 3. DBSCAN"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3.1 简单例子"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.cluster import DBSCAN\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = np.array([[1, 2], [2, 2], [2, 3],\n",
    "              [8, 7], [8, 8], [25, 80]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DBSCAN(algorithm='auto', eps=3, leaf_size=30, metric='euclidean',\n",
       "    metric_params=None, min_samples=2, n_jobs=1, p=None)"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clustering = DBSCAN(eps=3, min_samples=2).fit(X)\n",
    "clustering "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0,  0,  0,  1,  1, -1], dtype=int64)"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clustering.labels_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3.2 复杂例子"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "from sklearn.cluster import DBSCAN\n",
    "from sklearn import metrics\n",
    "from sklearn.datasets.samples_generator import make_blobs\n",
    "from sklearn.preprocessing import StandardScaler"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 生成数据\n",
    "centers = [[1, 1], [-1, -1], [1, -1]]\n",
    "X, labels_true = make_blobs(n_samples=750, centers=centers, cluster_std=0.4,\n",
    "                            random_state=0)\n",
    "\n",
    "X = StandardScaler().fit_transform(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 计算DBSCAN模型\n",
    "db = DBSCAN(eps=0.3, min_samples=10).fit(X)\n",
    "core_samples_mask = np.zeros_like(db.labels_, dtype=bool)\n",
    "core_samples_mask[db.core_sample_indices_] = True\n",
    "labels = db.labels_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "估计的聚类个数: 3\n",
      "估计的噪声点个数: 18\n"
     ]
    }
   ],
   "source": [
    "# 统计基本结果\n",
    "n_clusters_ = len(set(labels)) - (1 if -1 in labels else 0)\n",
    "n_noise_ = list(labels).count(-1)\n",
    "\n",
    "print('估计的聚类个数: %d' % n_clusters_)\n",
    "print('估计的噪声点个数: %d' % n_noise_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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UUoxWPbiYPnTAzEHq3fa7Cm9PagwUhhGuQj9QpeHKH+lDjVn4IqGcm9Qqvkgob5IDOBhefPo5RlXEON9bd6IMlW1HQtksvyFAysEYXnjq2SM2prZEs7rElVKJwHDge49dccAul+9FNFYSjmtMV0qtU0qt27t3b3MOT6NpFaalpvJc+mxDweg5g/VHCt3pQgcW4O538DVjD7dH7fjDVegHqjRciSCEmvpavlqyEIvVyrb87Ue8dMM3a751jrlUqqiinnS2uCnb/5DNcLo5zWhxEsW3a747ouNqKzRbIphSqiPwCXCniBzw3G1wiqH3WUTSgXSwOYGba3waTWsyLTXVUBh6zmD9oZRiovTiM3ZymfR3nreN37mGgYbnDKAzG9nHaHw7c12FvkNpRBMW0LgAe7SRmVGnj2wxR2tlTTWRhDj9HxfThzBMzCGHEirpQRSX0IeRKhYRIYvdzGYzB2vq/V+8HdAsKwClVCg24Z8hIp8aHFIE9HL53hMoaY57azRtGdcZbKCMoBvVNJDl8ifka8Y+jjiWU2QYruqK60rBoTSCYSP7GEgXzivvxkkDBzNp3B8aJYoFW9/I3/GRHcI5SL2b/2OkiuUxdQZvqfE8ps5gpLKtrhxmtIvoTRhm7QimeaKAFPAWkC0i//Jy2JfAlfZooJFAuYjsPtx7azRtHccMNhgiCKEBK3PVdlZKMSLi08wzmBjqsZKF7z85V6EfqNJwICIsp4jxxDGGOLpJONaV25hx6VUM7NOPnJwcZ6TTjEuvInZFEY9Xj+B1GcPj1SOIXVHkdiwQ0PFmUXxIXlARS6PpQYwp3OkIbs+lp5vDBHQ2cAWwWSm1yb7tASAeQET+DSwAJgN5QBVwTTPcV6Np80SFR1BVHbypxYyJWmngA3JZwi5OIMKrmcekFLfJMJ5iAwheM4HH0oOP2E6KdGcwMcwllyx2+zUdAWSxmwaEQdjMWedITzaxjwcqhrK6spSRp9hyEC6s7OGzvtHqylJGnT6S/33wPlf85XK/9ZBWUcJccplCYlBmtAnWnrzw1LP8tm9fuy49rRPBNJpWJJDMX09WSTHr2csdDOMg9SyggCx2cxxh/JORXgWhUZJYBCFU08AG9rKUXfxGLZfRjzEqzs2u7q98xOfs5D5GEKtsOREVUsf9rGG2Go1VhHv5lvNJNMxE9p65nMSYAN7LSiliCUU8zhmYAlQCFVLHrIiNxJ5w4jHXJU13BNNo2gh33HsXWdFlQZlaVlDMBHqhUBRykL3UoIA9VHMLmbwkP7FZfsPqcc1YFcljnMGf6MsKirmbb7mRldzPGn7kNy6jPzcxiPfJZaUUcyIR3McIFlPII6wlU0qokDoaxEqF1LFSinmYtSylyE34gz0iCAtgC1GNJtRwJVEqVY0K1N3BUE4kktEBmnTGEEcYJn6hLKDjHeOrrK32m6R3rJuHdDlojaYVmTBhAqaYKFZXlpIi/gWew9QSQziz+N45m7+GgW5F2j5hO3PJ5TYZ5iaYFVBGLb9Ty1QS2ckBblfDAJswfoL1RGDmY7azjCLOpRf3Mpx8KlhGEXPJpRYLIZjoRyf+TF8G2UtUuFJNA+GYAUeIqnsmslWE1ezmQ/KwINRh4QPy2MQ+DlAXdBLaOIljOcUM4fiAzqmmgagOEcSecKLX0tPtoTOZVgAaTStiMpmYt2QRo04fiVTYMlW9mVqy2M1n7OA6knmGjYamGad9XLqTRQlPsoF75GSOI4yN7GM5RTQg3MtwXmcLf6IvYBPIz7MJQbiYfpxNLNnsZznFfEgeNVgIx8wAOnMCEayhlDM4keUU8xpbqBH3ukVl1JBEZ6BxiKrDFGVFuJR+jSqMLqSAxRQyQLq4KS9fjKAbH5Lndb+nmamGBkJrQwiPjOC/HfK4urafe6G+tNlunckAkunCFVW2jmVaAWg0mmYhKSmJ1T+sYeqESWSVbXF2AHO1z6+gmAaEexjOK2x2hjx6YiToHmEtoZjoThR/pA+DOZ4sSpxOW4Cf+Y1KGriUvs7rDuF4wxl1qVSxnr0spIDzSDBcfeyjhr/QD3APUT3kV+jdqMKop/J6ig3cJyMCUgIOk5NVpNFqxNP34RyvtYGN2ftY0EHxmvkXqqx19IvvzXNptiS9K664gv6McbuWzTy0iWMFrQA0mqOApKQkvly0gHNGj+XzqnzmWnKpw4IJxUC68Ce7qcVXyQevgs4umJdTxFzyOIMKVlDMfYxwCssvyacLYXSmAy/JT07l4VmNdA/VAQnwVZTwKTvoL52dIapREuoWr+8NpRSjiQNRzGYzj8rpfp271TQQYvcDuCotX45s53hrbeP92LyTe/5xP5ddfjnQPjqT6SggjeYowL0NZCy7qeJpNlCLFRNQg4UOmAnFRBShlFPnJqBPpiufsp1L6OszYmcVJXxAHrcyhCHqkKC8WVYSRSgdCXVGCHkqj3qsNGBlComBlYiWEpawi26EM5xudKGDs4+BZ2tKI2WjgEdYy5/o6zZWI1ZJMYvZRTUN1GF1Xi8ExRmcyGX096tEVkkxH6sddO8Vx/yli5u1wU1LovsBaDRtANcOVstWLqdBrIQTQiwRFFFJBGZqsVKHhTBMhGCiE2FMIN7Qbl6PlbsZ7tdkskqKWUoRj2KbWZdIJQ/zA3F0ZA/VXoVxFiW8Tx4PcirdVZTf5xMRHmEtp9CN9eylCx0YTjeS6NwoHNVI2dzGMHL4nU3sczqqvd3nAdZgQZhKos/r+Xo3jvEOVF3YeNxBVv+whnVr1wbdmay10QpAoznKce1g5bD5RxLCWvbwDlvpSgTn0ovhdOUAdTzDJi4xMLs4sDmJS/jMIx7fCIeg+xN9iSGcNNYRQ7jzfr6Ep6fycGDkewgnhBOIwAT8Ti1VWLibk5lt92H4Wqk4HN4zGMrz/MhsNdrr86yUYuaRz1OciVk1jmx3vZ6/d5MpJWxiHyebuvFtr1qyd+QatpFsSsvKliIYBaB9ABpNC+Nu7jmUwVoilbzDVi6nv1PQW0V4kg1c0ox2c0fY5EIK2cVBLqUfo306ZHc7HbKj6cEKit1s7f58DwsooAoLtVh4m2wupncAz9IDBN4mm2oaDP0SY+nBb9TwKTuYyamGwt/zev7ezXC68iF53GYdSlbZFpYuXcrEiRPdjjmWwkP1CkCjaUEaGhpIiOtJ2J4qfnUxtyTRiQIquIDejHXJlvXX/9cT19m9p93cfZa+n2q7XyGZLm4tJ41w2PMf5XRWs9tplgk0WziLEt5lGycQSRpnBPwsD7OWEiq5ggGNVieLKGA/ddzCYIYq/wX1fL0bBw1i5SZW8aYaRyYllI7tycLl7s1jAm3x2VroFYBGcxSSk5PDOSljsO6pYKyHHf8r8imlijEe2bJN6cw1Xno2SoryN0v3ljjmIIXuLKeIXyhzzpI9u5D5GlOSdKEDZiYRH+SzxDGPfLfre4aLvs3WgMJFvb0bV1wT2IZLV2YZ9A2wZQ8fG+GhuhSERtMCOMw+5+zpTBpnNGr7uI8aziOhkXBsSmeu4XQlh9+d3731CvbWcrJUqhpdUynFeGzC0xFz7ysk1RWHorDaxxYMI+hGpZcqp7byznFcTB9ms7lR6QsjPN+NJxvY60xgc5SL8MRfi8+2hFYAGg1HpuaLo5b9pHF/4KQBgziv/ATGKGMnrjdB3+TOXPY6PK6z9FF052fKeEl+4lbJ5DpZzq1iqx30M2WMortXYWoVIRwz29jPHWRhRZjNZgB+psyn8HUoijosTXqWOnzX7U+hOyGogGoB2d6NsUIREb62l7SGQ+UiPPHV4rOtoU1AmnaLI5Ijt2AnESqE8dKDu5vJqeca5dO7IowTiHAz7xhVwPw73zFA3O3xTe3M5TBj2ISvif50Cqh20AyGOk09Rk7ev9C/kS3en/nIYcb6gLzDehZvBFILyPG+l7ILE4rrZLnT99KH49jBAbbyO7VYeI0tDJDOdCWcM88Y2ehajt8JWxTQJrfs4baGdgJr2iXukRy2JJ//kO1sH3g4Tj2HuWfKgVjOlhP5JxuwIM4Y+zB7QldnwviDn9DLD8njZLoGVS7aEcp4uxrGM7KRgXRhOUUBh16Op6ezSFzgTl7vYZa3SiZPMpL/sPWwnsUXruWnPTEqg+3pTK7Dys0MIZFol+2FhJzQiWVZK1ukvWVz0eJOYKXU28BUYI+IDDHYPxb4Athp3/SpiLS99ZLmmMGo0Nc1kswcchhJbJOdelarlYnj/sDk8hNIohMP8gNWhPNI8BvTbxR6eTF9WE4RKWIsfD0REZZRxJ/pS6lUkcN+9lMTYPkFW6jkIgoppzYoJ6+3MMtSqXKascYRxyf2hjOBPssiCjmTEw1r/LjizbQTUCkI+/uezWanAnNsX72vtEV7HLc0zeUD+C8wyc8xWSJysv2jhb+mVfFWB76ESuCQUy/YHrZvv/02NSW/kURnp+P1CUYyWvUgilBe5Wd7TL/3yB5H79oL6e1sbv4o6wxr/HuyihL2Uc0+qnmJH7EAHYJol5hCd8IwBeXkdT3XhK2wHBwSvmGYqaKBwcRQg4VVAbYDX0UJNVhYxx5m8b2hc9qBoxaQK54KzN/79vR/KKVIke5MqYjl/AnnHZM9hJtFAYhIJgTRjUGjaWW8RXJ0J9Lp1Jt+2y0B97C1Wq0sWLCAO6ffQhk1PMJaLrQnPDkET7ACdQw9iCWS6xnEOOL4kDyvglBEyJQSvmAnw+nKB+QhQARmt1r8VhE2y2+GjuDN8huCrR9wOGa+YmfwIaj05C2yKZFKp/BNpouz17AgfMJ2MqXEaxMcx7N8wnbCMPGgnwglsEXvmHEfZ1MUmJEzOcUai6XsoLOH8LFESzqBz1RK/QiUAHeLyBajg5RS04HpAPHx8S04PE174qG0R+0+AJw+gDfUL5RLLV8klHP3bf/gqcfS/PakXV1Zypmnnk50x2j4vYo/S1/CMbOQgmaL6V/Dr9yuhjmrbKaxjjvlJBKIptrFb9CAOE0YxbKWccSRQY4zuijQXIBrSaYeKwUc5LYgwzZPsdflf4L1HE84KXSnCx34hO10JoxIQrmTk5jNZlsTeXFvTen6LA9wCq+zhWz220xQXjJ5bQ3pi6nGQqaUOK+3lF3NkkOhlCLlYAwvPPVso6zgtk5LKYANQIKIHFRKTQY+B/obHSgi6UA62JzALTQ+TTvDKJLjlbS3mJaaitVqZWCffkytiLV16fKQH24RPNbfqalo4EDFAQYTQxc6sJyiRh2woHFjlEBwJF2BTRCNJQ4EnmUTdViIIIQkOjvLRTsE4x6qGU5X3mErkYQEbQtvQFBIE8M2LfQimrH04GfKWE4Ru6niNbZwOf3prqJ4TM7gF8oaNZzxfBZXgeyajOYqoLPYTT1WOmBiE/uc1zMBNzCoye/bbbuXpLC2TosoABE54PL/BUqpV5VSXUVkX0vcX6MxYlpqqmHo3pIlS5CySkZZExsJ/0Bm0bup4lqSG133cGP6HYyhBysp5o/0ARQrHF25aCBcbHVyHOGT4YRwkPqgnLkiwlzysGBtUthmCCZKqWIxhYRhZjw9uZZk/s53ztWISSkGSQyOGZ4jFHabPUlLgMES00gBuioE1+ijccSRT4VbtNB1srxZ3rdju1FSWFunRRSAUioW+FVERCl1Ojbfw28tcW+NJlhefPo5RlXENJrBBzqLvp4VhoLncGP6HSilOEVO4A2yiaGDoSLaTSUP8QOJRDOfgqBs4aPpwWJ2UUYNG9ln2MzdGw5bfAPCJOLdIp1qxOLWGSwQc9QtDHETyA7/xiopdnZJu5fhvMLPXGbvQOagud63Y7tRUlhbp7nCQN8HxgJdlVJFwENAKICI/Bv4E3CzUqoBqAYuk6M5AUHTrvlmzbc8zgi3bcGERIaLseAZQOegBepG9jlLEzgolSqWUcQl9GGMn7aKH7KdYiq5hD5B2cInSi/mkhd0CKrDFn8lAxjtUtQODgnkSmkI2Bz1DJsIc4lVcYR7/shvTlNRFiWUUcNv1CAizus11/sG2Kj2cdbIMwO+TluhWRSAiFzuZ/9sYHZz3EujOdJU1lQ3msEHE1HiTfA0JQ5+OUXOxu1wSBFdQu9GAtYVR3loqwhzyG1SDZ4McqjDQha7AxKiDlt8CKriBx5cAAAgAElEQVSRAxxs72UDe1nCrqDMUZ+ww5kHUE0DEYRwuxrmNAF9zk5uYQjvk8tyijnH7lgeTXc+Y+dhvW/H9syOZbx63wt+r9HW0LWANBoPosIjqPJIKgomgmcccSynqFGY42BiqMdKFrsDGkcWu90at4OrIgpsVjuGOCxNdOZaEM6kO5+xI6Cwzc/YwUhOJI6Ohu9pHHEsoIAwTEGZozoT5gzN3MBeBOE6Wc5NrOITtnMxfRhEDI9xBkl05kt2cj9reJnN7KGazADzDozeN0CWqZSQ4zty7rnnBnSdtoSuBaTReHD2yLPYuKLIbdYbTATPYGKYSy5ZlNiatNgxKcVtMoyn2ABCwKUVXEMemxJKGibmRiYpbx28HG0g4+lIB8ysZw/3MjygsM17Gc6L/MQl9HEbg+NeyymignqmkBjU+M+xO34HSwxLKeIqBnIK3dxKZyymkFsYwjr20JVwSjiUL/ABeSA0anrj732LCFmmUuZHl7J6yRrDzmBtHa0ANBoP7rj3Lmasu4qUikMCOpgIHldBbxVhjIvAjlWR3CcjeJmf/ApUo7o6TQkl7U8nN5NUIA7Yg9STSDTl1JFLOY/hP2xzNbv5nVo3c5PnvXKaUN56BN34iO3OmfypnIBJqUb+jsdYR0dCSaGHW72flZTwEdtZRCHnSYLb+17PXhZTSD1WZjCUroRTIXVsVPvIii7DHNOR1UtsvYHPn3BeUC0gMzIymDlzJoWFhcTHx5OWlkbqUVYwTisAjcaDCRMmYIqJYnVlqS0PgOAjSmJVJDNkKM+yiZWUuAn6KEKYQC8WUMAnbOcDcqmxd+caSJdGMf2uNCWUdDxxzCWPFOnOr1QH5IDNpIRP2MF0BvEW2YBtxWLUSct1Bq1Q1GAhFLNh1NQ7srVJ5qhqGvjc3u/Y8704/B0isJRdjKK785howjifRCZLPB+Sx4fk8QG51GIlHDP96UQPotjdSXi5biuVtdVEdYjgrJFn8sp9L3Duuecy9/33g24BmZGRwfTp06mqsq1ECgoKmD59OsBRpQR0NVCNxgBnRc+KWFKssbzM5iZVstzIXs6xN1LZyn5qXZK3xhPnFPQOYXkRvX2aKm4hk6c5k2jlWxEZtX8Mw0QYZv5kr0XkD0cD+FsYwitsdqum6TqDXmkPx5zBUD4gl+F0YxTdmcX3TCTe7Z05KoP6G78rFVLH3XzLI5wecLN7by0fXVtbmpRCREjruIVXP37Xa5ZvU1pAJiYmUlBQ0Gh7QkIC+fn5Pp728NEtITWawyQpKYnVP6xh6oRJZJVtoXdFOMuCDom0RZQMJobfqGEnB3iQ0wyFmKtpaDGFTDIwVSyiEBP4DW30ZuJZz17mkR+wA9nRAL6MmkYmoGoaMKPoQycupg+JRLOJfRRxkN1U0Zkww6ippoRmbmAvg+jSLC0fPbOJA3HwNqUFZGFhYVDbWwutADQaLyQlJbF1Rx5Lly7l+SefYe/KVayixFaOwQ+ZlFCHlb1U8yA/IGBo0zdqDGNG8QG5zCHHHlZpogdR1GPhbGJ9xub7SlZbI7/yB3oF5YAdJ3EsYRcD6UIC0ZxMV8qooYoGuhJBIRW8zGanP+BKBvAO2/jMSyG5poTCrqC4UWimN7yVcnB9pvHSk2UUUabqAnLw9ovvTW5BOckcWgH4awEZHx9vuAI42uqbaQWg0fjAZDIxceJEJk6c6DQLmSpMpFhjvZppVlHC++RiQvEx27mKgU7HpSu+nLErKWEBBVzBAMbYa+p8yg7+Qn8e5AfD2Hx/yWpNcSA78gFuZKXXukOe3CURPMQPhs7eQxFSgeUWZFJiGJrpDW+lHFwZTlcyyKG6VydWL/Ff59+ocOD/InfyXJr31Ka0tDQ3HwBAZGQkaWlpAT1HS6EVgEYTIJ5moZSKmEZmmmUUIYjTXn2dLOcUuhkKf28z9SgJZQ2lXE4/p61+hdjCP83K5DWU1F+yWlNrEVkQrmJgwP6P7ioKixjnHgQTCruKYj4nn78bOH69EUgLyQhCsCghe0duQKGdTWkB6XD06iggjeYYwtUs9MJTzzJrzXdU1lZjEkWcRPIX+rnNjo2ih/zN1I2SvVxn795CSZdR5DNHoKm1ccwo3mErH0ieM09gsI8VANia0Hi7VyChsEvttYgmkeDX9u+Kt1IOns/UMTwyqLh+b4UDfZGamnrUCXxPjr3MBo3mCOMwCy1cvpTyqoPMmz+fhI7HM4tTGaKOdxOMDqenK/5m6kbJXp6z91gVyWOcwZ/oyyb2cT9r+IUynzH2RmPxx3p7cbdwzPTlOLoSzsc+GtOAbXVjQvm8l9H4b2Ql9/AtG9lLHRYm0ov17PGageyJw/E+3o+P5lit69MUtALQaA6T+/7vbvZXHOB6VjBLvmeNlDr3GZWF8JfNu80gWcoxe3fFpBRD1PHcroYxW432W/LBW4kKbzgcsDcxhCc5k1M5gRx+pwFhJLGGHbocq5uz7M5qX/dyHf/LpNCLjsxgKMPpRihmJpN42KUzjJ4pM6qMO++7O6BrHutoBaDRHAZzMjLIz7Z10XqdsUwjiU/Z4VQCRvV/jAS8K0a2+kBm70ZKwpWm1CKyIAzleKJVGKNVDx6yt2e0zbR7uvXQhUOrm7/QL6h72Zy9VvZRzWfsYAZDCVEmbmMYn7GDVVLssxbRKil2nufLNHUs1/VpCtoHoNEcBo/MfJAbGexMEkqmC9dIMnPIYSSxhk5Pf85YI1u9v/DJUqnCbDe7eIuuaY5aRI4qnQgsoRAzii38BmJrTLOFMhqwcjurSSSaD8lDRHwmt2VSwlzy6EgIH7ODicQTRQgNYiWKEGc/ZKNSDhvZxzKK2Es155HAiRjX7G8PdX2aglYAGs1h4C1JqIRK53dPp2eYDwcpGCdLDSaGDHIM8xBKpYp/sp4zONFv/X5/DtgN7HU2WjHKW3DgSKjqSZTPxjQHqedD8ljCLiZKvGHdozqsWLFyBQNQwApKWEyhs95QH47DgpXL6c888skghwashGGiI2FUUk8tFuaRzzKKOF1OYAoJRBHqfKaVEXvpcELngMI+2xNaAWg0h4G3JKEeRLkdF6sinX1w32Gbz5m6t9m+IHzCdkyinLN3t4giejCL7/3G2DvG8j65fEieU6AGUovIgaMr2UIKuJR+fhvTfMwO1vCrYSG5JDpzC5kMUzaz2FAP81imlBCCiWGqKyulhKkksoZSt9IUrkpnMYVksdvZM7kbEXTt348fNq7TM38Pmqsj2NvAVGCPiAwx2K+AF4HJQBVwtYhsaI57azStyUNpj3LndTdzdW0/Z5LQf8huVBIZ7E5PjucKSXIWZzOaqRslS22hjEhCuZOT3Eozh2PGbG/AooIw8WRSwveUchLHs5MKyqkLqkaPVYTv2M2l9GNsAI1pEMUSdvESKY0US4XUeY3d92zSks1+dnCASwLoJuYwYUURwqzcjVr4G9BcK4D/Yuv49a6X/ecB/e2fM4DX7P9qNG2aaampiAg3XX09lZZaehDFJfRhpIr1es5+VUeFqYHVcqjaqCtGtnpH5FB3FeVcSSynmBz2cyn9gyo3vZRd7KeWeKKpxsIk4nmH4Kp0Opy9np2/vPUZSKIT9VjYwm+NZvi+YvddM4GtIghi74bmv5sYArPZzIOcekw2dG8OmkUlikgm2Fv2GHMh8K7YWAN0VkoF1hJIoznKSf3rX1n/y0907dSFc03xnMGJhseJCJlqNwuO+5VP5n/JvOhSMtVuw+iWWBXJvQznS3byAGvIZr8zcsg1fFJQjSKKjGLsb2IV97OGTezjfBKxIhRS4YxI8hdB5MkKihlPT7cZeKlUMYvv+YTtnExXnmQk6YzlSUYynG6EYeYNst1CR73F7osIK6WYueRxBidSST0/sY8udAi4mF0K3QlBsYl9x2RD9+agpXwAccAul+9F9m2NYsSUUtOB6XD0FU7SaLzhr0yEZ4MRf8c7nLGhmOhLJ76l1HCG7i2iyGFuMqqK2SBW0tmCAGGY2UE5SR5NY8B317BfKOMqBjiP9VXawtU0s4oSnmID94nNwbyKEg5STy860iBWD0e0lWn0d9r1LQiXu6x2/OEo/LaQQsPErzkZGfbyDoE3eTnWaCkFYPQTMwzqFZF0IB1s/QCO5KA0mmDwJzC8lYnwbDDisEUbHX+wpopQZcZkFaqxEGGPGAr3EjnU1PIOHex/+n+hH5+yk2oa2EO10y/hr2tYCZU8zUZuk2GcQITP0hYOlFKMJQ6TKGbzE+dITz4gjwjM3M8a6rAQhplQTNRi4VaGMFR15WzpzlxyWUlx0N3EHIXfXvdI/JqTkRF0k5djkWZrCKOUSgTmeXECvw6sFJH37d+3AWNFxGeWiG4IozlacBcYLhUh030XBWsqixYtYsalV/FAxWCUUrwkPxk2pPG23ReZUsIKiulCB5sZSYQsSnifPCaTwGmc4HU278Bxzmfs5GL6sIJiHuTUgEs8P8Aa6rDN+F9VYxrtz6KEOeQyjf58ST6hmNhDNemMxawCt1w3iJUbWUmDxeLmBG5KkxdoG20eg2kI01Ju8S+BK5WNkUC5P+Gv0RxNPDLzQa6o6k2y6kKIMpGsunBFVW8emflgs93DarWyaNEizht/Lpde/Ed2VOzhFjJ5SX6iN9EsY1cjf8E44vjaYLs3RIRlFFFFvdPurpRitIrjMvqxgHyeZSMX2x2t3gS645yL6cPHbGesl0Qvb+dOIp5edKQOq9drTyaBOeQylUSeYGTQfgqwrXaiDAq/2fI3OrltszV52en1Wo42jwUFBYiIs81jRkZGUGM6mmiuMND3gbFAV6VUEfAQEAogIv8GFmALAc3DFgZ6TXPcV6NpKZrSFSoYcnJymHruJKz7K0mpiOFxRriZXJZTxD5qmEcB55PoPM8WMirOBDFfdvtxxFFGDZXU0wFzo5o5o+nBfAoIQQXlaF1AARFBipIRdONDtnsN/7SK8D2/chn9nWGmAyT4bmIb1T5GnXlWo+1NafIyc+ZMt/r+AFVVVcycOfOoWwUESrMoABG53M9+AW5tjntpNK1BUwRGoDgazUytiGWUNdFno/b3yeV7+ZX91DqFewLRfMJ2Dkgd3/OrV7v9h+SxjxrMKO5muGFz9SgJ9VmozhOlFJMknu8o5XQv0U9G2Bq3NHCSF5u+UZhpU7qJZXYs49X7Xmi0rylNXtpKm8dg0JnAGk0ANEVgBILVamXqhElMrYi15QR4kWtKKcYQhwjMI58nOIOOhDqF+z6qWUABl9HfZ1buKkr4jB1ex7OHqqAdrafQjY/wbjc3opoGQjB5Ld1sVDE12G5ivgq/NaXJS1tp8xgMOjVOowmAaampPJc+my8SyrlJreKLhPJmcQAvWbIEKatklNV74pgrY+hBR0LZxUHMykS0CmMU3QnFxOV2c4kvu/1YFccf6duoiqeDGixN6hpWE6RtfgN7CTcwQzkwqphqUspZHTRTSnxWB81Uu5kfXcpXSxZ6zQCelprKQ2mP0i++N3mFO3lk5oPM8WHPT0tLIzLSvTbS0djmMRj0CkCjCZCmdIXyx4tPP8eoipigY9uXU+yM8XeYSwK1jTsKuf1CWaM8AW/hpr5wzOZFJGDTzEIKqaCem1hJJ+nAaLpzHgmE2CN8vOU3+Mt0Xs9evone75Zv4Y1gQ0HbSpvHYNAKQKNpRb5Z8y2PMyKoc4bTlQ/Jc37312DGEyMl4uAEIoN2tK5nL6Eow0qlRmRSggnF64yhBgsb2cciClhEITfLEIao433mN7gW1ltOsbPAXAfMKJPi04++csu38IZrZBfYSnlfUWXbbqQA2kIIaLBoBaDRtCKVNdVNNLlYnN9d+wUHiqcSAdvMvIp6vmZXUI7WxdicoJ+xA4RGPgjXYzMp4XN2ch8jCFVmQu0rF0fV0NlsZoYMNSyJ7YpRpnMmJZSO6cnEiRMDegfBRHY5QkAdUUCOEFCgTSsB7QPQaFqRqPCIJsW2u4ZP+mswY4SnEgFbB7AQTFiQoLqGmVH8kb7U0MBHbOcR1pIpJVRIHQ1ipULqWCXFPMJallJk2GfAEft/Of15jS2MoTtLg8xvCLbVY7/43uRS7rbNW2SXrxDQtoxeAWg0rcjZI89i44qi4GLbPapnNrUchEOJiNjyCD5hO3dyEuGYeYZNfjt5uZZcjlWRZMpuxtKD4wl3Mc00YMbEoAD7DIymB4spZBUl7KOGLFXKaPzXjWxKq8dgIruOxRBQ0ApAo2lV7rj3Lmasu4qUisBNLq718cG4g5g/NrCXPhxHppSwnCKqaKAX0TzPj9RgIQwTH7HdaxvG5RQ16hq2h2pG0I1oFeY0zQRbqkIpxUSJ50PyeOXN17n/rnugAlKssd4VURNbPQYTCnoshoCCVgAaTasyYcIETDFRrK407g3gSRa7nfXxHTQlQWohhZRTSwgmrzPzF2QTJxLJJnsSmWcnL89zjExRTfFPjKAbGeRy3XXXkZKSElSF1WAJNLIrLS3NzQcAbT8EFLQC0GhaFZPJxLwlixh1+kjE30zXo1G7o+zDcor4leqAo3BWUYIV4RXG+DTHbKOc6xgUcJcwI1NUU/0TFnuNoGArrB4pjsUQUNAKQKNpdfz1BljPXpZRhIDT5OJZrnkqicxmM0rwabfPpIS55DKZBG9Jx87jaoNMCjMyRTXVP2F2iU8xmUxMnDgx4OieI0VqamqbF/ieaAWg0RwF+Jrp9o5PYM+2ai6nHycS4bX5iiNBagXFjRKkNrCXr+0F5VJJYhGFbGCvYSLVCoqxIEEnhY0jjo89TFFN9U9062ycIaxpXrQC0GiOErzNdK1WK316JTCvpIAVFFNJg7NcsytGCVKOLN0OmDBjogELAtzLcPKpYBlFzCWXWiyEYKIfnfiz3b4/m81BCe/BxPAOW8lkt7OIW1P8E4vYxW13PxDYS9McFloBaDRHOSaTiSUrlnH2aWfQ9UAIDVR5LdfsmSDlaL4yjOPpQRSfhhbwWf0O5pJLHVbCMTOQLownrpFTd5wEJ7wVEBkZxReqGFWtSLHGBl3ALZMSDpot3HfffYG9HM1hoRPBNJo2QFJSEt+s/Z6dHaqZSHzQzVf2UE1Wx/08/9psiAzjeMJ5g7HMVqO5XQ1jiDq+kUN4MDHUYyWLkoDulWUqJbJbZ75b9wPf9KrhiegtrGY315LMZ+xglRT7LOC2Sop5nzw+mf8FISF6btoSNIsCUEpNUkptU0rlKaX+brD/aqXUXqXUJvvn+ua4r0bTnkhKSsKiJOhyzSPoxjZ+J+T4jlxzzTWsWb+W/eZ6Mv0Idkf1zY/Yzko/wtu1+ubAgQPZuiOPVz56l9JxPXkxPJty6phLLg+wxjBL+AHW8JF5J18umn/Yzt6MjAwSExMxmUwkJia26Y5dR5rDVrNKKTPwCnAuUASsVUp9KSK/eBz6gYjMONz7aTTtmcraptUOqsXiLI08cOBA1v/8I2eeejpSqRjjo+9vjqmcsMhIMo+r4puKwGPxPf0ZVquVhQsXcudtd/DBzjzeYxsWBDOKzlHHccffH+Dvf//7Yc/8j9WaPUeKw24Kr5Q6E3hYRCbav98PICL/dDnmauDUYBWAbgqv0bhzXGQUj1ePCDg2H6BC6pgZvoGK6krAVgbZlv26k0hTGJEhHZhc26OxYO9oE+xfLVlIv379nBFK33rE4t95390tEosfCImJiYYZuwkJCeTn57f8gFqBlm4KHwfscvleZN/myR+VUj8ppT5WSvXydjGl1HSl1Dql1Lq9e/c2w/A0zcWcORkMSe6P2WxmSHJ/5szRS+uW5uyRZ7GRfQEfv0ZKeZR1VNZUMSCxL7fecit3TZ/BhQWd+LeM4WbLIARhQ7KZWREbudmUyayIjZSO7ckrH71L9o5ckpKSnDP6hcuXUl51kAaLhfKqgyxcvpSJEyceFcIfjt2aPUeK5vC0GHmjPJcVXwHvi0itUuom4B1gvNHFRCQdSAfbCqAZxqdpBubMyeAf997JG3eNZtTQSazeXMIN994JwLRpemndUgRTO2iNlPIpO7iWZFuxs4Jy3vj3W4ySWLca+FfX9uOLqnLKqw62xCMcUY7Vmj1HiuZQ20WA64y+J7h7l0TkNxGptX99AzilGe6raUGeeOxh3rhrNOOG9yI0xMy44b14467RPPHYw609tHaFs3aQqRSwCflZ8j3XyXJmyfeskVLnsfMp4BqSSVZdCFEmklUXbpBBbMB9ZW2rgb+zRZ/jSHEstm08kjSHAlgL9FdK9VZKhQGXAV+6HqCUcq1ydQGQ3Qz31bQg2Tk7GDXUPY571NAeZOd4bzCuaX4ctYPmRZfyX9nKp+xgGkm8zlimkcSn7GCNlCIilFBJfzq5nd+fTuzGva69txr4bZHU1FTS09NJSEhAKUVCQgLp6enaAeyFwzYBiUiDUmoGsBgwA2+LyBal1KPAOhH5ErhdKXUB0ACUAVcf7n01LUtyUh9Wby5h3PBDi73Vm0tITurTiqNqnzhqB50yaBg3Wwa5mXOukWTeJptl0fuIqupArqWcZLo4z82lnAgVSrbs91sDv61yLNbsOVI0i+dGRBaISJKI9BWRNPu2B+3CHxG5X0QGi8hJIjJORLY2x301LccDsx7mhucyWbFxF/UNFlZs3MUNz2XywKyHW3to7ZKkpCSqrHWGM/wyanjlo3f593/f5H+RO8mW/TSIlWzZz/8id3L1TTfwRUI5N6lVfJFQznPpxjXwNcc+Ot1O44bVamXJkiW8+vLzZK7+hoqD1UR3jGD0qLO59K/Xckf6p2TnfE5yUh8ef/oF7QBuRfrF9ya3oPEMv39CH2f8vVIqoIYnmvbJ0RG7pWkxfIVy5uTkMCQ5iQf+dgPnD7KQ824q1UtuJefdVM4fZGHJF3OwWoXs7Gx+zs7Vwr+VeSjtUcMZ/kNpjzqPmZaayrb87VisVrblb9fCX+OGVgDtCEco54vTh1G1+BZenD6M/5sxnV5xsTz37LOcfebp3Hl+ImtfvYTrpgyha6cIQswmunaK4LopQ1j76iX87YI+jEk5i5ycnNZ+nHbPtNRUnkuffcyYc3QJh5bnsDOBjyQ6E7h5GZLcnxenD3Nz5K7YuIsbn13Gwep6pp6ZSPo9/ptqvzl/Cy9+lc/mX7YdNQlAmqObOXPm8FjaI+RszSNpYD9mzXyIadOmOfd7lnAAW/imjuAJnpbOBNa0EbyFcu4sPUDGrEms+aXUy5nuXDd5EB1ULUuXLj0Sw2wXzJkzh+TBAzCbzSQPHsCcOXNae0jNiuvzxSf25LY7bmX6wxezeEc60x++mHsfuNvtmWfOnOkm/AGqqqq46qqr9ErgCKIVQDvCEcrpyurNJSTHx9hi+gv3B3QdpRQ3TR3AKy/960gM85hnzpw53PvA3T4FYlvG8/n+9sxfCe1gpmxvOSGhIQw/O5m7nrmSx9IecZ7jrVSDxWJh+vTpWgkcIbQCaEcYhnI+8zX3//U0pyIIlItG9SHrm2/9HqfrBzXmsbRHuOuZKxl+drJXgdiWMXq++56/nvde+sp5zNDT+5OzNc/53VephqqqKmbOnHlEx9xe0WGg7QhH1M71f7+HgqLd9OneiYevHklsTCQ3PPM1j113VsDX6hQVRsXBKp/H6PpBxuRszWPo6f3dtnkKxLaMt+crzD20+tz8Qy5JA/s5v6elpTXyAbiii7kdGfQK4CjCarWyaNEiLpgykc6dOmI2m+ncqSMXTJnIokWLsFqth32PadNS2VlYwnvvvcfe8mqueWopl/xjHvdefiqXnzMg4OuUV9YR3THS5zG6fpAxSQP7sfmHXLdtngKxJWluf4S354uN70ZDfQMbv8nmuXveZdbMh5z7HSUczGaz4TV1Mbcjg1YAR4CmmD38xeA/8LcbGJKc1Gzhl9OmpXKwuoHqJTMYfXIcZpPCahUW/ZDPhTO/JGbqa4Se8xIxU1/jwplfsuiHfKzWQxFjn6/eQcrZvlcMun6QMbNmPsRz97zLxm+yvQrEluJI+COMnu/pv70NDSYm9plO+sOf8fQTz7pFAYFNCbzzzju6mFtLIiJH7eeUU06RtkZGxnvSO66rfP2vS6Rm6Qz5+l+XSO+4rpKR8Z7Xc7Zt2yaxJxwv6XefKw3LbxfLijsafRqW3y7pd58rsSccL9u2bWuWsXY6Lkp+/Xy6zH/qQklO6CLJCV1keP9ukn73OfLr59Ol9uvb5NfPp0v63edI/56dJSo8RADpEGqWLtHhcuWVV0p9fb3X6w8e2E++/tclbs/x9b8ukcED+zXL+NsyGRkZMnBQkphMJhk4KEluvfUWGTgoSZRSclznjmIyKRk4KEkyMjICup7FYpGFCxfK5KmT5LhO0WIymeS4TtEyeeokWbhwoVgsFsPzBg5Kkn99eJ+sKP6v8/OvD++TgYOSmvX5An0OEZH33ntPEhISRCklCQkJ8t573v92NI3BVoMtIBmr8wCaGW+x9nek/8TP2YeWxY6SC6+89DxfL1tOXb2F6MhQUk6K4+YLhzHh1ARM9ln5knUFvPbFT2T9WExFVT1hoWb+cM54br39b7bywE2Mxb9gykSmJDegBP72Sib/mjGa66cM8doe8O0FW3jw7e/49LGp/LzzN579YAO/7q/mg48/N+zj6u4D6GHzATyXqUtIeOCYhY+78FSWff4d9zx7HUNP78/mH3J57p53DWfLruTk5HD+hVMxhVqZetVoRk0cQcfjIjl4oIrVizcw751MrPUmvvpinrNlowOz2cziHemEhB5yBzbUNzCxz3QsFssRe2bNkSOYPACtAJoZs9lM1eJbCA05ZMusb7AQOfFV5x9UTk4OF50/mXBTLTdPHcCFo/rSuWMHfj9Yyxert/PaFz9RU2fhuVtGc9ermYSHmbn5wmGNj5u3jRprBz7/akGjP+xAePPNN7n7zhkoBU/dOIrrpw7xf878n3nxo438+PZfUQremr+Fv72SyVzCQuwAACAASURBVKeff+VVCTzx2MNk5+wgOakPD8x6WAt/D5IHD2D6wxfz0qz3uP2xvzL87GTnvo3fZJP+8Gdkb9lmeG5OTg4po0dx5T3nM/myFK/Ke8HcLN595iuyMle7/a447h3MPb3hmNS8/MqLrM76hoMVlXSMjmJUytncdusdhzVZ0QSOVgCtiL8VQE5ODmNSzuLRK0dw7eRkr3+w/3xvLf+cs5YXZozh2smDvR731vwtzPzPOrK+WcPAgQN9js210NuKVVlYGuq5fspgvtlcwg+vX+63w5Tjnqfd+D5p15/NxNMTAHjjq83cl/4d+/YfOOym3u0Rxyx8Yp8bWLzjjYBn41arleTBA7ng+hSmXD7a733mz1nFV29/wy8/ZzsFsWP1cdczV/pddTh+f16a/SKZqzKpqqwmJNRMSIgZqxUiIjrQtXsMF107LqhVyLFARkYGM2fOpLCwkPj4eNLS0lotg1lnArcivsomW61WLjp/Mo9eOYLrpgzyKnBFYM6ybTx/6xiu82KSAVtC1vVTh/DY1acw8rQRbN261Wsk0fgxo+jbuxcP/O0GpiQ30CMmnBdvG8PO0gPcdOGwgIS/4543XTiMV7/40bnt+qlDOLFLOE899VTwL6yd4vg5TTn/PCKiOrD5h1zi+/cIKjpoyZIlmMOEyZelBHTPyZePRoVY3TK4p02bxtNPPEv6w5/5dNDm5OSQPHggd9x9C4PHdSfju6dYmv8mH61/nlsfnUa37p0JjQhh1r9vZMrlY+gUE405xEynmGimXD6GVxf+gwuuTyFl9KhGgQyu76JT5+Mwm8106nwcU84/r9mi344kjjIWBQUFiAgFBQVtJnmtWVYASqlJwIvYGsK8KSL/z955hzV5fm/884a9nHXVuiturLOtgqPWBQjuhRNwj6p11I11Vq2rAirDUQG3MlWsgyG2bhEc1I2rbmWHkPf3ByZNIIEk2n7tr72vi0tJ3p1wnuc559z3vazA+2bANvKtIJ8D/URRvFPccf+JKwDQnvY4dOgQs6eM5LR3jyID7qHTd5jrf4rTG/vrPCv/zCOI1Oc5lC1TmpLmolpq6dz1P3CeGcaSEa3wdGrI4TN3lccv220DKUHD+Kikhc739+x1FnUGbeV5+Gjla/4Rl1myI5k793WTk/g3o2DOXp4nJ8Q7ig7dv9C5BhAcHMyEb8bx8sVrqtX+mEETu9Gh+xfFnjti+wli916mXPlyOqdplGmmqc75g4jWNFMsgcv3sWbPTKrUqqjx/AVXIe9Sv/hQUL16dY0+xNWqVePOnTt/+/X8rSkgQRCMgBSgI/n+wGeAAaIoXlHZZixgJ4riaEEQ+gM9RFHsV9yx/6kDgDa4OHWmW/08PJyKzrW7zg7DpVXNYrdThX9kEhtDE3n6OpubwcMwMsr/I5bJ5NQZvJWZbs3xdG5U6PgmHdaRFT0eYyPdF4O5sjysOnsjPTpR+dqz11lU7RNAZraUQ4cO8b3XXK5dTSYrJ5dcmRxzUxOafGbH7Hnf06VLl39tLlhbzv7ogV/Zvi6cuykPsS5pSfrrTCxtLFgw/3u+nfKt2jE0pW1WTA3EY0avIgeB1JuPmT18DaJcpP84R52CrSFppt1+hwk8ukjjZyyKImO7LmbNivXUqFHjneoXHwokEgma4qggCP+T1cvfnQJqCdwQRfGWKIpSYAfgWmAbV2Dr2//vAToIuuYc/h8hNv4krva1it0u7tIDnbZTRXf7Wtx69IaPSprzy7lUAFJSX/Kp22bMTIzUBhPV49tYmPAqPUevc73OkGJjaar2WkkrU6SyPMqVKUHfXq48e3CDoZ3r0b5JFUpYmpKTKyMxMZFhbr35pHIlrl3795nCyeVyurk6M2RaN5wKzKQ7dP+CzccWc+z+ZsKSvTl2fzNj5vbDz39ToSCiSWph2kp31s8L4vTxRI1BJ/XmYyb1Xkq/0V3ZFrdM5zSNIWkmU1MTzsYma3xfEASchziw9qfVWp9Fwe2dBrRhyFRnXLp3+yDTQdpIav8E8tr7GAAqA6kqv99/+5rGbURRlAGvgbKaDiYIwkhBEM4KgnD26dOn7+Hy/jcomIuXSCRkZGTSYlQIpRx9iiRZpWXlUsra7O1xdCNnlbQyJS1TqszPp6S+pP2kPZQvZcm3/Zqq/YGpHt+hcWVC42/qdW8H4m9ib6dO8HqdIcXU2AjEPL4b2AJTEyPiEh/Qq82npAQNIyt6PDdDhrPYsxVlLfJo2awxhw8fNujZ/lNhSDDNzk1n6dKlysCXkpLC9au/a5RaeP0yHf9le3H/ag6pN/9MxcnlcuZ6rMN9ek+cBrbVK9j+tH4tTkM0z861HcN16FeEbjmqdRv7Lk2JORH3zvWLDwWLFy/+x5LX3scAoOmbUXA9pMs2+S+K4iZRFJuLoti8XLly73xx/wsUZPUeXOpEnSqlaFjjI+YMbsmNkOFkRY8nJWgYLq1qMsc/ATv37aSk5qtxKmblKakvsXPfzhz/BFxa1VQGUk37KWbl3e1rEXfpAT3nRvC9+5fcePCq0GpCddY/xtUO39BEjUtYTRBFEd8DiYx1baz2+oG4G1iYGTG5dxO8D1xicp+mnNk4QKOxzMUAN1aOcaBXD5d/1UrAkGDae2QnVq9bSb0GdTl8+DAObewpX7mMxmJxtdofs/GQF31GdWZS76XKQeBsTBKm5iY49i8+hQN/BtvDhw9z4ngMsRFncK43hg5VhuNcbwyzh63RutKA/ACfeFo7Y93KxgIRud7PwnmIA+vWr9Fp+78TChmLatWqIQgC1apV+8f4GLyPAeA+UEXl90+Ah9q2EQTBGCgJvHgP5/7goGjznOxSkzM+PXGwq0zPuRFM6duUs5s0B8QzGwcwuU8T2k/aQ0rqSxwaV2ZT2GXaT9rD5D5NtAZS1f02hl3G3u5j5UrAwtQYd8cGarN9BVRn/Z2aVyNbmkdglOYle0EERCUjzc2jY/M/l7eiKLJy53lKWZkRfPQa37t/qVP30sox9jh2/vqDXNbrAn01dOLjT2Lfuale53Do2oycHCnd3O3p2bsHQ6Y6M2JmH1ZMDVSTWlgxNZBBE7u9ncW3xX1aT+Z6rkMulxO69RiuQ7/SK9i26mqH26ABlC5vQxvnFgSdXE70bX+CTi6nVecmGlcaCljZWJCZnq31+BlpWchyZXo/C/suTTkZX7wC7f8Cbm5u3LlzB7lczp07d/4RwR/ejxroGaC2IAg1gAdAf6AgbTEMGAqcAnoDx8QPmYBgIAq2ecrlonImXlRBVxAEPJwaIgK95kawdGRrhi6NZvloe932E0VmbDzJ9tldeJ0hpYSVKaNdGyGKYG5ihMusMH5NfkRaVi42FibUqVqan/ZdxN2xARKJwL6FzrSftAcR8CiCc+AfmYTX5l85vqY3Esmf2/hHJPH4RQbDutTn5OVHuDs20Ol5jXBuyNq9lzhy5IhGEtmHiuDgYL6bNZ379x5SqVo5Zqz2oFylMkyfNhVAK2s3PS0D6xJFC+gVhCKYlv+4NBU+KavWhbNu7nbu/f6QqrU/LlQAdhzQhtCtxzgbm8yl364zfZWHzudMvfmY/Zt/wX1GT5zc1FNGinqBY/82RO2IZVLvpYW6fjLSsrC0Ntd6/NiD55Dlygx6FulpGXrt8x+KxjuvAN7m9McDh4GrwC5RFJMFQfheEASXt5sFAGUFQbgBTAG+e9fzfoiIjo7GwkiKu2M+qzL67F3lTFwXeDg2wMzUiAu/P6V8KQvd93NqSLlSFkzxPkHN/oFk5shoWKMsdu7bqVrBRi0PnxI0DHfHBqQ+Scc/MgkA2yqlOb6mN2t2X6DFqBD8I5N49jqLXFkez15n4R9xmfpDtrFuz0WOr+mNbZXSQP6g4Bd+mSk+scjlkHL/FaNdG+k105zUq/E/ylhG0YEzecUgom/7MXX5cLb8eIAXT18Xq+lvbWNF+puiJbQLQhFMQ7ceo5dnR+WzVRSNj6ZuZvOxxYW6f1Rz8Vnp2ToHW0W9wGN6L5wHtSumXqC+0lAg/tB57Fpq7tYRRZGdvgcxtzAz6FlY21jptc9/KBrvpRdPFMUoURRtRVGsJYri4revzRNFMezt/7NFUewjiuKnoii2FEXx/6UcpM9PqxntZKv8o/ENTdQ7II7q1ojVu88ztX8zvfab2q8przJyqVzOBmmunJ5zI5jcpwlJWwYXSh+NcG5EgndfZvklsCn8MqIoYlulNImBg1js2ZrwhFvUGbQVq87e1BqwmWm+8XxevxJHV/eiRqUSykGh3pBtzNgYz54FzmRJZfya/Ejv7qUebT7VyVjmQ4G2Dpzt68KL1fS3d2hN/OHzep1PEUwv/XbdoJTJhYRrGJsY6xxslfUCHVo+oXDXjyiKHNhyFNdhHTRuHxUSS25OLmaWpgY9i9b26gq0/3QS2f8a/85m7L8IBds8DWnn7NHmU7Klefq3gTp8ijQ3jyl9m2BlblJsHr5O1TKcXN+XJdtPU3/INvwjk3iRls1XTT8hYHpHfhjVGrtaH1G1vA0rx7bhZXo2dYdsxaLTeqr2CWDRz2f4/f4rHu4dQeeW1bCxMNFYbygOuhjLfEgoyuykOE3/CeO+IWJrrF4Fd0Uw1WcWr4CFlRnZmdmUKFWCAV9M1amIa0i9QLXrJzI4htxcGc3bqK9eRVEkMjiGwBX7WLFjGpZWFuwLOKLXswjfGsvE8ZOUrynYyZOnj6d+u0psi19C9G0/tsUvoX67SkyePp56Deq+Nwn1/4/4bwB4j0hLz1ILgIYGRKksz7BAmimlSnkbalQqoVP6yLZKaW4GDydPLrL10BVqDdiMZaf12LptIfzUbRZ7tuZS4CA8nBoQutiF5aPscfqyBpnR47mz052SVqakZ+cC+YVlSzNjwzgFxRjLvE8EBQVRvXp1JBIJ1atX15uuX5TZSXGa/p06dUKeKyFqR5xO54oKiVUGUwtrc71SJqk3H+PZcR6Vq1dg2DQXQn5dyaGbm/j2h2E8++MVcz1+4uuqHjjVVR8QDF1pJJ5OIXz7cXy8QujQ/QvSXmUgy5Xx+kUa4UEnGNFpHrv9DrNmz0yq1qrEgLGOPHv8iqgdsTo/C/KM6NixI/Anoc7Fwx6fg3P0lp/4D/n4T7nrPcLG2oJX6TlKWQVFu6U+MguKfnpD9rOxNMU3NJGJvT7TeQZnZCRh+oBmzAk4RblSFkT9MECZ41eFKIr8tO8Sy0f/2bft0Lgy++NuUKW8DS/eZCMIEBp/Uy8G84G4m8Uay7wvKDRbFLaDCs0WQOeujbmz5zN9mjoLd+E4XyzMLFn+Q9GyzRJJPsvWoY09iGLRsgohsQSuyJdVkEgkNP68DvGHz+M0oG2x16ggfQ2f1lNJskq9+Zhvei7F1MwE12FfFWIB+y/bi8+CHQatNKxsLMh4k4Xvgh0YmRizc8NBdvhEkZ0lxdLanI+rleePB89ZHzpHWSx2cGyG78IdBC7fByJFPovI4Fh+/jGCuNh4JBIJcrmcro5dGPytE04DtT8PBa8BUcSlezc1Ebz/kI//nsZ7RPOmTdRIVYaSrCqWsTSYnKUt7VQUoayElRlvMnK4tm2oxuAPf3b6KNo/5XKRmpVKMmNDPL3mRvBr8mNEEeYFnOLgb7fViG3aIIoivhHXGTdxyttj/rWWmLNnzy7kOauv4bgm8bT1a3x5kPqoyOCvgK2tLXGx8YQFxDO83Wwig2N4/SJNOVuODI5hVBcv5WxZETDz0yzHik2ZKElf03ri/Jb0pRgQ+ozqzMZDXhpnyxsPedFnZGeMjI0MKs5aWJkRdGo5o+f2pVLVcpSrVIYtx5cQfsWHjQe9GD2nH/NHrld+hlY2FmRnSVmzZya7Nx1mVBevQs8iIugEgx2+I/CHfWoyEIGBgUjFrCKDvyo+ZBLZ/xr/yUG/J6SkpPBFy+ZULmPKxQA3BEHg0Ok7zPFP4IweUsvNR4bQs82n7I+7ofd+S0a0xnlmaCFtn5TUl/ScG4G5qRGjXBpR0sqM7UeuEZ+oMJiRYGpsxMqxDgzv2kCtxVMURQKikpkXcIrMnFxeRY4lJfUl3WaGIQgwrX+zQj4F6/ZeJE8usm+hs9YBBcA/Mpm14Xe4fOU6N27cKNoj4R29D+DD0myRy+UsXbqU1etWkpMjJTM9G0trc+xa2uI6rAPN2zRQm63K5XLcv5pD7xGdcHZrp/W4p48n4v/DXjYe9FLel/tXc+gzqrNOq4eRXebjOvSrQtvK5XLOxiQRuvUYl367TlZ6NhbW5jT+vA6Vqpbj4d0nLNk6GdAsDCeKIqO6eOE5szct2zXi9Ys0BtnPIPyKT/6xY5MJ3XKUxN9SyEjLwqqEBQ1b1Oa3Y4kYGUvo1KkjE8Z9w9dff02FSuVx/667TvejQGRwDHH7kilbtsz/e6+C//wA/mbI5XIa1rPlm241WLvnPJP7NMHDqSFyuYid+3bl78VBYbZywd+NzzyDdN7PL/wyU33jyMjOxdTYiHu7PZTpI4UkxPfuX2Lf6GN6zYvUajCzYsc58uQioYudKV/aigPxN9kQmkiONI+dXl35zCOYpC2DaT9pD17DvyjSPSwgMpn5m0+ptY2qvR91hfnbLhATl98BpItHQmDUVeZtO09MXIJBg8CHptqor9Daz2vDCFkfxbgFA7SmTGYNW0Przk2UwbHggFAcfjuWyPr5QWyLXabcPvXmY+Z6rNOaPtrpe5BcqYzlQVPV+AAKYbjRc/oR/vNxLiRcJTtTiqWNOZWrV8DISML6sDlqgff1izTcWk9nns8Y9vpHcyHhGrJcGcYmxphbmoFcIDs7m93nVlOyjE2x96N63L4tpjBx0aB/pOKoPtBnAPjX1AD+SsMGRf+/p1N92jb+WI1UtW+hM20n7iZPLjLCuYiAGZXM/MD8gGlsLNGZnOUXkcRs/wSiV/agWZ0KuMwKU+bhVYloDnaVlQNBQYMZBbPY3bEB/pFJtBy9A2MjCW0/+4TFnq3p2LwqL9KysbYwoefcCBa4f4lnMQQ1T+eGyEWRTlP3c3pDf0rbmPE6Q8qBuJtsiLxOjmhOTFwCn376KQ3r2SrJc0Ud08OpPiIiPVycuHzlepEzNk2S3IsXL1arAcD/VrNF35pAxNY49u87wMRJE4jYFofzEAfsuzTNz8GnZRF/6DznYpOZoUL60rerp0W7huRk5xIZHIOzWztl+sh9ek8c+6tfnxopLCSWCd0XUbPuJ6Qk3VWuEIxNjFkxNZDhU3swfZWHWuDdF3AE96/msDBgonLgCN12jDyZnHVzgxgwzpFZP41S22eHdxS5j6W8evZGrwHAysaCXKmMchVLY1PKColEUoDUFodDG/sPVnH0r8K/YgVQsPgH+X/470uvo6DMs2rKZaRLI34IOkueXM5HJS0Y7WpHd/talLQyzQ+IKrPsvQVSJqrHKbjf/tgbrN9/CblcVNtPNe2k0P3/1bcfjT30W1Gs23uRS4GDlOkg/8gkAiKTkMlEvXwK6g3Zxr0/0siV5WFuZkKHr9ozbuIUOnbsiEQi0dkjQfWYLcbuZ+kaP63s4aK8iEWRD8a5SYE/NfFFjUE9YlscokxC2IFwbG1tkcvz89nr1q/h2NHjSHOkWNpYYNfSllNHL3Hktj9Gby1JneuNIejkcr2C5ZXzN5nS5wfGLxjIHv9ondNH4duPs31dOBui5lOilLUyaO/acAhBENQCPRROFYmIjO7ixbgFA4suCgfFELhyH2v3ztLqO1AQr1+kMeCLaXxSowLSnNxC1wKaHdP+ifgvBVQAf/XSv1RJa1K2ual17cjlIkfO3sNryylepuWQvGUwv5xLxSf0EkfPpb41gTfF3u5jxro2pmPzqmq594LHmeN/kuQ7L5DlyTExltCgelkWerQqtJ9q2iks4RYurWpSuZy13gYzLUaGsHhEvu2josZgaW7MsC719fMpiLjM/M2/UsLKjEcvc3hTgMqvySNBLheJPnsX39BE4i49UEpYODSuzBhXO+49SSfqqhFhkZrVRIuz5fwQIZfni6/N95rL1WtXycmWIsuVYWpmSpMmnzF3znyNPgolS5VgW/wSZYAvGPA7VBlOtMqAoAtkuTI6Vvek/MdlMLMwZWvMUp2/N6p5ftXXizKLiQyOYdemQ2Rl5DBkkkuRNQ4FwoNOsNc/WqvvQEFEBseQEH2BRZu/0Xotql4F/yRpkoL4zxKyAO7du6fX6/qiYP8/gEQi0LllNcqXtmRa/2YYGUno3LIaoYtd2PO9E3a1PuJZ2ChCF7vQuWU1jcFfcZyvm1Xh0YsMqlcqgZmJEVXK2/Dbhv4a91No+8wLPMWJC/dxta9lECN5tGsjpe2jQgAu+fZzgwhq2dI8pvZriixXWqgfuyB5ThcF1NW7znMiNl7rOa+m3MK+kbpctX2jj7ma8mES0OVyOYGBgbgNGsCjp/cZPa8fu8+t5sidAHae+RH7Xg349ruJGklNBfWFFO2iCujLH4D8rh4TU2Mq16hAvzFd31kKuijZCMjv0slMz8bMzETnzh7ngW0xMTXW6jugClVCXVHX8iErjv5V+FcMAH+VYYOibdHG0oTyrhs1avVrastUVeAsTu//2t0X1BqwGWsLU77t2xSHxpWZVoxMhELbJyM7n4gWe9EAgxmHT4lPfIhf+GXmB55i70Jnw5m+mVI8nBqyalwberg4qf3RqQ6eioJ1cQqo2gYTBerZ1iT+srogbfzlh9SzranXtf8dSElJoVbtmkycNB73mT2VZi02paw4F5fM8ikB+H6/g5TkW6Sm3qPF583w9/dHLpcjl8sxNlFv2yzYLlpwQNAFcQfPYWpmQtLZ3w0mhWmCNrMYQRAoU64k/cc56jfYDCnad0ABVUKd6rXIcvP4uqq7Gju6VafPPljF0b8C/4oB4K8wbFDV/F8x2l6rVv+bTKnG1cG+hc7M8jtJzQGBzPHTPNudtekkn4/ZgadzQ65uG4KHU0Ol3k5xA8enlUtRwtKUF2+yScsqfA3FIb/WkMOEtScoW8Kcmw9fvbN72AjnhsizX6oZnCjIcwWVU4sSIdM2mCgwa64XI36M5fiFVHJleRy/kMqIH2OZNddLr2vXFfrKQiuQkpKCvUNrMrPTGOc1UK1v3/2rOfgv20urzk2UUswhv65k5Jw+fL90LvUa1CUwMBCbklZqAb5524ZIc3LzmbPozh9QQBRF9vofQZotRSbNM1i9VBOKMot5cPsPg6SyLyRc03pvqvITC/0nqqWKBEGg/5iufNGhsZrE9aRey0h7k67XdfyT8a8YAN63YUNBzf+itPotTI05d/0PjceRCAKzB7fkjBafgHN+A/lxbBt8QxP5/f4rIF9e4smrTJ2MYprYlmdp0BnMTIwMCtxW5iY0qFGWN5lSxq0+jijyTu5hgiAwuXdj1q9eRsN6tqSkpNDGvjWh8Tf1Vk4d4dwQMyFHI7ln4EA3Fi1fwzebErHs7MM3mxJZtHwNAwe+/2KvQh10pFcPDt/axEivHkyfNbXYQUBhD9nGpSkly9ooxdd0IW1tjV2Ki4c9kyZ/Q9M29Qj+KZJZw9bgXG8MHat58OThc36aF0T49hM0a9Mgf0DQUXIhMjiGp49eULFqOcwtTQ1WL9UGbSuErAzDGMjZWTkaSWTaCHWqcOjajMtnflcjw/Ud1QUTM+N/jXTEv6IIbCjkcjnR0dH4/LSa2PiTpKVnYW1ljomREUs8P8fTqfhg5Rd+mcXbz3ArZLgyX28oP+BS4CBKO/tiZW7CQo/C7ZwK5PfMJzPL7yQSiUCVcjaMcmmkt8l8eMItDizqRmBUMvMCT9GxeVV+vfKYq9uG6E1Q69yyGpBvHl9n0FaWj27DvG3nWbh4GetXzKdKWTNcWtfU8xqTibgi0VoM/jtQr0EdRnr1oEnresrXLpy8yiav/VxNvq51v0OHDjFlxgRKVbBW9u3rS9oK//k4GxfvovRHJeg3pitWNhYc2ZtA4m8pZGZkY2xshKW1BZ9/1YgzMUm4T9NuCaloNd24eBeW1uakvcqkYfNatHFuoTfhKiH6Aou3TNL4vixXRudaIzl6L1Dtdee6YwhK0K9bSUEmm+szBq8R6zEyNiIzo2hCnS7XEr79BJFbEv6x3UD/8QDeA1JSUtSYqf4j3Shlbca+2Bss+fk0Ho7ae9ZV4enckFW7zxN99i5dWlYHDPMJ2BCaSPSZu5iZGOluMCOKTFwXw9oJzVgeclbrgFEQCtvHJSNaq5nVrN19gaycXPwjkxjh3KjY42hyD/uzJlCfPFHOgnlzePP6Jddu5xH5623Grz2OhakxdaqWZu6Qz+nSsrrWAnl3+5rM8NMt3fJXQZs6aFGy0PCnPaTv9zuUffv6Wjc6D2rH/s2/0GtEJ3ZvPKwkas1Y7Vmodz4jLQvv+cHs8D3IgLGOhVpND2w9ypsX6cjz5IiiSFZGFt2Hf83mFfsL9f9rg6LY2sapObOHrSnEGHYd+hW1G1XTuEKoXKMC8YfP07Wfg1bGsevQr2jetqEyKMcePIeJqTFPH75AYiRh+8kf9BpAtK1WnN3aErU9/h9nVGQI3ml4EwShjCAIRwRB+P3tvxp5/4Ig5AmCcPHtT9i7nPN9ITg4iIb1amNkZETDerUJDg5Svl7n0+rUrVuHzNdP+LZXA7X0TNAv15igh9iaIAhM6dOUeQGnlK8Z1pVjx+Ltp6n8kTUeOqw8IN8oRirLo7t9rXe2fVSY1cwb+gVTfeLwi7hcZO7VPzJJWTxWDeCvM6RYmpvQYcpepvnEYi7JYeVYB+7t9iD7yARSd3uyYowDmdkyBi8+TJ3BW5VeyQXxIUhJa1MHta37aZG1AYU9pKr4miFSzD09OrJx0a4iU0bb4pYxYYEboiiSkZZJ2M/HGdR6Bp1rjWRQ6xmE/XycjLQs3rzKYH34HDLTsjG3NKPuZzXIzMgmMihGp+uJDI7hUeozYiPPqtUuVHPso7suoHYD9eYL6LLipAAAIABJREFUURRJe5XBDp8ojbUPTTaUoiiyz/8IPdw7khB9AQQM9lrQ9Fz/Ld1A77q++Q44KopibeAo2p2+skRR/Oztj4uWbf42KIhCa0fakXl4LGtH2jFn+iTGjx/HnOmT8BnXjKzo8QTM6MjcwFOEHP1zKW+oxn/S7ef4RSQjipo7g4pDd/taXPj9KeN7NtYrQFiZm/AmU6psDfWPTDIocCsGobCEW0wf0JzZfgk0HPZzYfewyCRajAph7e4LGmUg9sfewNzUiEu/P2XlWAeu/TxEq3H88tH2vErPwWHCLo2DwN8tJa0Jc2fP58dp29T8eX+cto0O7b8usjagaN9UbdM0RIrZoWszZLl5OA3QnNqBtwFtUDvGf++GRCLhjwfPQLGpAB9VKMWE793IyZby5P5zyn1chs9a1ePk4QuIchG/pbuJDI4p8nsTEXQC7/nB9BvThU2HF2gVnBs8yYVb1+6reQlHhcSCAM8ev6L3yGIE694a3v+8OpS8PDlu451YvGUS83zG6F3sLsq4pqD/8P9X45l3qgEIgnAdaCeK4iNBECoBJ0RRrKNhu3RRFK31Pf5fVQPQRhTq43WQ3V5dCxOI1sWQuHkQACYd1hUSWysOubI8rLr4YFurBuYSKZeu3yfriP7HsOy0nkf7R+olE+044wC92nyKh1PDIpnFRTGSFVDk769vH4qt2xa+7dcMnwOXyJbmkZYpLZbYJooijd238zpDypwhLXVKI/lHJrFo22mszY1J3DxY3Yv4A6gBQH4heOHiBaRcu4Ft3U+ZO3s+CxcvKLI2oCBwLZ8SQKu3NQBDSVua8tiaIIoiI7vMJzMtm2+WDFYjayny6XYtbWnVuQnlKpZm7eztWJWwYI73aOZ5/JSfYhr6lVr6KPbgOfYHHOHJo5f0G9OFwROLn99FBJ1gj380Ab8sJCo4lvVewZiZmzJydl+cdeABhAedwMcrBJ/wOdSom/+3qm/9RKFTpI1IJsuV0aXWKGQymQpTW47z0DYfvJbQ31kDqCCK4iOAt4NAeS3bmQuCcBaQActEUTzwjud9J+QThbqovWbf6GNepWVpJhDde6H83VCNfxtrS5KupnDkyBG6uzgbdAwTYyO92znHdbdjincs7o4NlLaPR87eo69XJDM2xKsFboXuj7acuyJ/X9LKlPSsXGa6tSDk6HXdi9kRSWTmyChtbVaklpAqPBwbsOFAIm8ypRw5e09ZTBZFEd/wayxb66/7w/iLMHDgwEJS0IMHDy6yNqCwh3Qd+hX+y/bi2L+NcjXwPvLYmqDond+8cj/zPNaRK5WpKXo2alFbaSBvU8qKrIxsBk5womqtSgQeW6RU7NywaKdSvdS2UXUepT6jYpWPGDShm07X4TSwLfsCjuDWagbmFqZ4ftebgzvidBLEg3wS2E7fg9z9/ZFyAJBIJCwMmMik3kuL9Rco6LWgCQr/YYXxzJBp3XDs76BdC+kfqiVU7BRUEIRfBEFI0vDjqsd5qr4dkQYCawRB0Jr/EARhpCAIZwVBOPv06VM9TqE7tBGFStlYaCYQVS2j/N0Qjf/9sTcoaW1FdHQ0HTt2xMrCVP92yrj81Im+7Zwt6lbk0fMMAiLz8/8KhrKRROD69qFIj07kefjoYhnJ8GdPv+JfVdaxfzE1gU3hl/Ha8ivVKpbQu4YyursdpazNlMxkAL+IJKSYKx2iFPirPQV0RVG1AfjTHlK1TdNQ0pY2A3ZNcOjajKyMHHaeWaWWXz994jK3rz9Q1iQkEgk5ObnKlJREIqFlu0Ys3jKJ8Cs+HL0XSPgVH37cOZ36TWvRe0QnvT7THh4d+ahiKQKPLeLiyatqhve67N9vTFdCfKLUXq9Sq2KR/gK6tIYqEH/oPK1at6KbqzNDpnVTGutoux6nAW0YMtUZl+7d3uk79q6Odfqi2AFAFMWvRVFsqOEnFPjjbeqHt/8+0XKMh2//vQWcAJoUcb5Noig2F0Wxebly5Qy4peKhjSg0cPBw3BYeUn99xS/MHNQCyG/fbFG3Aqt2ndcr1+gbmkj3Lyoxa/IIGtaz5U16Nj4HEvU6hs+BRLKkMr0HjrCTN7EwNWb+ZvX8v6FmNfZ2H6v19itYxz/uukDdwdsK1QT8wi9Tf+g2lmw/w/E1vbmQ8sSg+seNh6+IT3yYr4AafpnpGxPYHxapNoNTJed1q59HyjY3sqLHkbLNjW7185TP/+/o8dZWG1BYRirsIQ/tOsnCgIkELt9HparlOLDlqF7fi9Ctx7TmsTXBysaCnGxpYW5BzFIGTeymZgiTXUxvvlwu5/TxRJINYAy36dqMG8n3cGkwjjMxSQbtf+f6g0LPqkqtigQeW4TnzN4kRF9gkH1+sbtviymcjL6A58zeBB5dVGTwF0WRHT5RXL9+nTu377Bq+uZivZTh3Y1nFKKVd+/eRRRFpWPdXzkIvGsROAwY+vb/Q4HQghsIglBaEASzt///CGgNXHnH874TtBGF1q/3Ji07j/FrjmPZ2Ztv1sWw0KMVAzrUUWrUhPxyjYfPMvCPTNLpXAFRyeTK5Kwc24YzPj2Z7FKT3Dw52VKZfl05Mjm5uXLW7b2oV4BYtesCbh3rcnxNb9bsvkCLUSH4Rybh9nVdvQch3wOJjHG1w/dAImNdGyvfs61Smsl9m1CmhBlh8Tep2icAq87e2LptYbZ/ArcfveH0hv7YViltsJxEemYuaZlSWowMYcbGk3iMGM3UyROUs/ySNlZ80aIJHRuV4Lf1PTST894+/7YOrf7yQUCTc9jyJX9aRiqkoLetCCfxt+us3vMdZ2KSeHzvGZEhupG2IrafICc7t5ABe1HQljISBAGngW2pUaeychVSlI6QKltZKpUZROLKzckl6ORy8mSGMY5zpTKNHUoFVytH7vhTqowNrTp+Rst2jYrt7Y8IOsGr52n0GNmWXWdXae1EKoh37R56H451+uJdB4BlQEdBEH4HOr79HUEQmguCoEjO1gPOCoJwCThOfg3gbx8ACrZ9AiRd/Z28vDySrv6uZIl2aN+WKX2bknt0IombBymDf/tJexjYoQ6v0qVM698Mr82/Ft9RE3FZraNGoWlvZmJE4IyOenXlBEz/GjNTI/Lkcp0HDr+IJO4/TePqvRfcevSai/5uLPZsTXjCLUb/eJSU+y/1GsikuXnce5JWqEVUFEU2hl7Ga9iXBH7XCQszY3ynfIVEIpCTm4csT06ZEvlBx1A5CWtLE0xNjGhY8yMQBGIO7cGpnowNk9rQ9fNqCKKM12lZbApP5JPe/gREJhWypVQ8/wVDmmiVkXifGDhwIFeTr5OXl8fV5OuF6gSq9pBLxwXQZ0Rnhn3bHe/5wYT/fLzI70X49uNsWLSTjr2/1IuspK31UQH36b3Y4XMwv2CvJSVVkK1saaDgnKWNBSXL2BgsWGdsYoTvwh041h5FhyrDNc7SRVHk4I44crKlBCzfV2w3U/jPx/FfugfviLk4u7UrshNJ0yBQsHtIH/zVopWa8E5FYFEUnwOF1p+iKJ4FPN/+PwEovt3jL4S6PnyXfH346flMxYLyAGMnTGbW5BFK0pRCo8Zr+Bes3XNRScLq3a42PedGsCE0UWNHzU97L5L6JI0E736FOmoqlrEk6fZzjq/pXeQxFF05x9f0JubifRpUL8v2OV10MooJiEpmtt9JmtQuT++2tfluYzwPn2WQk5tHZo4MGwsTWtaryBz/BATQqr+jalYztntjFmz+leNreqvVClR5A4FRSZgYS1ix4yyDO9XjeupLTly4ryx6K1JPehnHx9+kVqWSXE99yZ6Y31k9vg1t7CqruZsFzuhYyN1secg5wpe6FHr+Ho712RBx/YMg+tja2nI1+ZpS3/9kfAKyXBm7/Q4Tvv1Eoa4bBWnr0b1n2HdtSlzkOQZN6KYXUWvErD6F3lO1fPzjwTM6VHXH1NyE+7f/UCOCKT2Hp/dUdtvoY1ivgOpAVMeuht77xx48h3VJSzxm9CrUleO3bA/r5wfzdc8viT94Hqk0l58OzAFgrsc6Jd+iYDfTrg0Hkebksj5srtYUkUJNFBHmeq4r1EVkZWNBegHJc11RtWpVjbL17ypaWRT+FVIQ+ujDK+wdJ7vUxMOpPodO32Gu/ym+9/iCeQG/qmnqK7T6fUIvEZ/4UK2jZoyrHbP9TrLYszUIqGnbmxlLqFzehmvbhiCKaD2Gop1SEKDOoK0sHtGKPu1sdW7n9J/+NY4zQnkePlpp0zgv8BS/rO5J+VL5xvOrd5/n4bMMqlawYXzPz9SPFXcD39DLPH+ThbWFCQKCWoto/jGTmP92UKj9SSnqDt7Klw0+5uz1x5ibGjPG1Y7tR64xqGNdPJwacuj0Hb7bGM8Ffzc95CSC+eNlJoiwoBh3M9X9/COT8Hp7bQUHgQ+lhVQTjIyMOHRzIxcSruX75J5OKeQZXKFyWSb3WYaxiTFDJ7vqJKOsrfVRm+Xjm1fpjO66gMEqGv2aLCZPH0/Ef9leNh7SzXZSFEVGdp7PiFl9aN6mAQM+n4qVjQUBRxfpvL97hzmMmduPlu3tNL4fGRSDz/chjFswkK79HJT3q+Y//Pa5mpmbYmJqhHVJK7bFLsXIqPg2XG3eB69fpDHUYTavXr4u9hgF8b6Mq/6TgigAbW2fV1MKd6NKJBIOhEfR1qEVIiLhJ28y2rURG8IuF2LvKjpqFK2JBXH/SRrDl0VTuZw1Y1ztCJieP0t98SZbmYsf4dyoyGNAvp7QoxcZuLbOL56qtnP6hF7S2s6ZJ5eTlikF/rRpRIB+86O4FDhIzQZyqncc8wJO8c26E0hz8zAxNsLEWEJObh5lbMzo6PApM91aUNrGjGevszjw1vw99Ukap96ucjaFX+bR80yiz9xhoUcr3B0b8Pv9V8zYEM+6Nxdxd2xAp+bVmOodq5ecxPM3OeTK8h3VhnWpT2OPIJ3kMBTH7zU3Qs3dDD4MGQltsLaxIjM9m5btGqkFl4JYu3cWUweuYP38YOSiqFQTLYiiWh+Lsnws/VFJVu6YzqTeSxHJb7/UxFZu3rYhPgt2ELUjVqdZvKo889mYJEqUsSY3R6bz/hFv0zjN22r+/BXEN1EU2bYqlC597ZXvKeoDLds1Unsu1W0r81X3z3UK/opzKJRNVT+j+EPnaW3fSqdjFIQiyP+djnX/rQC0OEQptIBu37nD3V0e2LptISVomF69+89eZ1FrwGZeRY4p9IeZkvqSthN3s8D9y6K9gt/OsNOzcrkZMlzv89cZtJXn4aPVjtliVAiLPVurDTotR4XQutHHxCc+1GuVM2V9DKvGt+X+03RmbownWyrD07kRP47N7+m2c9/ON70/Y+2ei0q+gD73PtMvAYkA9auXZVDHuoa5m2m4XwU5TybL0/l56gpNxLCC+f+i4NStK/XbVSo2GMrlcoa3n03dJjU5deQiJUpbMWCsU6HURtjWY+Tmyljor26DqCt5SrFCkEgEHt9/RsivKwtxFZQDybSeOvXgr9o9gz9Sn7Hqu60MnuSCXcs6Ou0fEXSCLT8eKLaNU7H9kDYzkefJGTjeiVadPuPahdsc2PILl8/8TnamFBNTYxq2qE3y+RuEnFpB6Y9KFnlMVbx+kUb/z6dSscpH3Pv9IVVrf4w0M5dAv208f/78nb4D74L/VgAFMGuuFyO0eMRqg62tLUlXUzAxMaaUtZnB3StZOTKNX+ZPK5dikWcrvtt4kh93nmPagOaF0i8bwi4rawDTNsRpzJ0XZZ9Yo1JJWjeqpLa9QtLBJ/SSWkC88eAVH5Wy0HuVk9onDY8fjlDGxpyIZa60n7SXuEsPlGqnFqbGeDo1pG3jT9RqFzHr+tBtZhird53n2/7NCt37T/sucf9pOmVL5B/3izE72L3ACY/lRwzSUSp4v68zpNhYvX8ZCYU89LcrhtCoZW0un/6d6dOmAhQKAAq12Z+81xIfdzJfHsLGijp1bAnxvlCsCNvZmCTMLEz5brUnoihy5kQS29eG8dO8IKTZuRibGGFqZkKX/g6MnddfPQ0Sk8TPa8J4dO8pq6Zvwff7HRoF1+DP1sqzMUl8N3iVxo4dRQ9+UTn2sG3HyJXKmLHak/me6zE1M+HNy3TsOzelZBmbIvePP3SeXRsPkZ2Vo1Pwh7d8gdFdOLwrnqP7T7F+fjBlK5RkwDinQmbzf9x/xqRey1gU+I3OPsO/HUvEwsqMiQsHKT/rxeM3EhYWRmjkfp2+A/9r/CtWAJBfCF6y0IurKbeoZ1uTWXO9dNKHV/j9GroCKDgDB3Wz91EujShlbcbP0deIv/yA9MxcTIwllLA0ZbFnK4Z1bYBEIhAQmcTKnee4svVPKWbV44xxtcPVvpZaIXTlznNIc+UcXN5dLQeu6bpMOqzDxsLEoHusPXALz8NHkyeXY9nJm+wj45nqG8e2w1eQ5YnKonMT2/LcefQGYyMJ0wY0w6VVTU5ffczi7ae5fPMZ2bl5mBobYW5qRJ2qpZk35AslOU0hwVHedeN7+Rz8wi+zLeENJ0+d0fk4ukBXeeii5AXiDp5jw8KdjJrVlwpVympVxjyw5SituzQtcvZeMD+vLd+vCIShW45pNU0HcLQdpXEFoICmHLullTlyuZyWX9kxZJILU/uvUKacvq7qriZ/oXH/t7WPcyevGDRLH/jlNMwtzRg+tUexctiKNJkug8DQdjOZtHhIoc/aa6Q3XpvG6S0R/r7w3wpAAwYOdDPIEERhWGJQ90rcDepXL4Pr7DDl7Nza3AQjI4HBneqxcowDRm/1gPq0+7M1T6HnPzfwFPZ2lQGYG5CAqYkRgVHJyjSKtkKoou/d3bEBgVHJtJ+0R60QqpB0UIWNhYlBqxwbCxPSs6T0mBtO3KUHiIiUcPShlLUZQzrXZ6ZbC8qUMFcOSj4HLnH70Rs2hV1Wq110aF5Vq4YQgKWZMa/Sc97JllIBURT5ad9FrMtV1+s4ukAXeeji5AWc3dpRvnIZvEb6UPGTsvQa0YnpqzzUArX/sr3cv/2YQd8Urb2jmp9Xplk05PvVZQ1imdR7qcZA2KhF7SI7dlRz7ApEBscQtu0YlxKuMeH4ZfqN7arcv6D8hab9FehQZTglSuknKWZhZYY8T67WtaQJCh4EaO7u0YTUm481ftZpr9MNkgj/X+Cf53bwN2PshMn8dOAKo10a4RuqH3Fq5c7z/PEiU8216/fgYfwwyp64xAc09ghSU7lU2Dx2nxPONN84nrzMoolnEG0n7qbfV3U4tLwH8wJPsSniMj3nhutsn7jA/Ut6zY1Q9sWr2jQq4NC4sjLI6oqU1Jd85hlMrcqllPeYfWQCd3d58L37l8QlPuCryXu59fA1ZWzMqVzOmsrlbJDK5Jz//QlZOTIszIyR5uYhiiIi2vuzLc2NCY2/+c62lJBfWJbliVy7rn02pk0uvDgUJwGhcAIrSl4g9eZjfpgcwDivAQQcXaRVGXPsvAFM6bMMR9tRWlmqCo2cgB/2MrX/8nxTmGKUQ4sycO8+7GtCvKP0Vt3sP8aRrMwcKlcvz8+rw5R9+9YlLPl5bZhOnAxD+AInD1/go0qldfZY0OZbXBCiKGJlY6Hxs7YpaV3kd+BDwn8DQAEU1JJxdHTk9oNnpD5J10tT3y8iiWxpntLLV5tlZPtJe0hJfalkGqvZPB4Zz91dHizybEXcpQf0nh9J4IxOLPn5NHI5ehnKmJkaceRsPqFEVcpBgTGudlibm+gsD6FYgXzbt2mx99h24m7qDN7KrE0ncW1dkzs73ck+MoF7uz1YNa5NflfTzWdM9YnFzn17IenngKhkzE2MWLf3Ig52hktYqBLr9nzvSHpGlsbttcmF6zII5EtAbFWTgFg4zpe7d1Jx6taVJUuWIDGR49jfQeP+an32WtIVoC7x/FHFMnzZ8TOtLNUqtSriMb0XZhamSuvJ4qAtENZtUoPnj18qPYeLQ1RILJkZ2WxZdYCyFUriMvQrdp9brWTXDp7kwunjl7Wya1VhiFbSDt+D9B+rp9m8Ft9iVUSFxFLCukShz/rHadsY7DakSBmQDwn/mhqALijoAqbIqZfttgErcxPG9WiMz4FLLHD/skgSll9EErP9Ezi5vq9GWWVV+EcmsTz4LBnZucX2tSusGetWLcPAr+sYbPFY0KYR8lcflXpuomIZKy4GFN2jr6+l5abwyyzZfoabwcOUKS9t9zaue2O8D1xS8goUJLSjq3rRe34EXzerSvzlh5zZOEDnLqBmI4Jp+9knxCU+UMpdlylhTp2hwbx8lVZoH0O6xhRISUmhXfu2ZGSmk/Ymgyq1KjJoQjdatm+U787lcxBptpSVO6ZrzDNr6rNXhSphS1EXMDYxxrZRNQZ948LTRy/YvHJ/ofTN7GFrlLLTukKTvWNE0AnWzd6OdSlLPKb3Krbjx2/pbgSJBM8ZRW8bGRLL5mLy76ePJ7Jh4U69+AJdao1k19lVBllNhl/x0Xpf21ZGEBcbz9mzZzV2+7xrJ9i74L8agAFQGL1/P6Qp7o711L5gmTkyTvn2o+/8KErbmLFo22/5ujjd1UlY+2NvsH7/JVKfpLF8tD3TNsQV6swZ42pHp+Z/qm4O71KfGRvi+WG0vW42j8C33rGEzOuq1/11t6/FjA3xGt2+8o8PZUuYc+fxGwIik/M5A1pgiIH7pvDL/HIuVWM3UUHbyfnDPqfT1P2ULWGGNFeurF/sW9iNVuN2YmVuoqyFFAe/iCRuPnzNJ+Wt1eSu/SOTcWituV9bH96IKorK7QN/5thDtOfYi3IFK1jALVgXCPhhL9KcXLoP61Aoj62QedYH9l2asmHRTuXvCpE0Y1Mjerh/ze5Nh7V27IRuPUZOjhTrklYMGOdYbP7deWBbRFHUmn8XRZEnD17wx4PnRIXE6kR8iwiKIVeaa5DOUEZaFpHBMYXuK2JbHKJMopR9trW11RjYNUmEf4j4bwAgf1bVvZsj3w9piodTYa9fGwsTypeyVJKvvA9c5MSFB3zrHcvEtSfIlcmxtjChTePKSCQCNpYmbAi7rEb+UhRB5/gnMNUnjn1vGbVHzt3jk3LWeOiRzlkRcpaz1//A8YsaOt+johA6P/BUISkHyE+zGEkk7JzvSF+vKOSiqLVH3/eAZkvLolpS7e0q433gYpGEN4X3cdUKJbAyN6ZXm9p859ZCea22VUoTucyVr6fsY/L6WORyEc+ieARRySzY8itnNg5QW4kV5yWgkAtXXQHEX35IPduaWq+9YG5fG4orNmoL1EURtgoWcAOX78OmpBVnY5OVxVRV60ldYWVjQWZ6tnLVEbB8H08evUAmlbF9TTjNHOpTr2ktTh4+r+YRYNfSFs+ZvZHnyQlcsU93j+OBbdm98RA+C3Yw+Jtu6jINvvkyDfM3juWHyQFA0Zr/4T8fZ+OinVhYW+DacDxZGdq9hQsiIy0LKytLrsQ8wm/xbGV7bmv7VqxZsZ6OHTv+I83iNeG/AQCIjo7GwkiKu2M9je8rOoCGd22AiIggCBgbCcoA17FFVca42lG9QglajAphzYS2Onfm+IYmMlFPffyp/Zqxfv8lvQaA1xlSjI0kfO/+JZ9WLqV8XVXrRzHTPr2hP07fhbJ2zwUm921aaJXzy7l7BMxQ1+Ev2JJacODzOZDIjQevSEl9qTUtpujZ9w1NZHLfpoQn3Co0UDW1LU+2VEaDGmWZ7Z/ASh04FAXPFxB1RaOXgAKG8Eaio6MxMhW15vYLwnFAG0K3HlML0qA5UGvS39EEVZ2abatDWTDKm+zMHCyszTE2MTbIbMbC0gz3r+YgkQj0GtFJa9uoT/i8QquZWUNXY/e5LXOGry3W4F1x/X1HdWH7unAO745XyjRYWJmRnZlDqY9K4L90L92HdWDnhoMc2HqU7kM7FNb08Y3i5fM0KlYtR0+PjoWu2X/ZXnwW7NDa6hp/6Dxt2joQGX5Q52f1T8V/NQDAxakz3ern4eHUUOMs1tzEiIplLDEzNVLq2xTsuVe0N/4w2l5nq8O1uy/w4Fm6wQzj11Fjdd7H760hi6WZCSbGApu/68TlW8+UgbKgDWRBBvCbjBwszU2wtjDhycssNUvLolpSFShOm0f13hS2k5o4FM9eZ1GtbwAju9kRm/SUFxl51P60FmfPXSAtPQNLcxPaffYJY7sXbinNH+yuMH/bBWLiEop0btKXN+Lk3JX67Ytn76pCU47dud4Ygk4uVwvUxdUFCkKhtXP/9h9EXPUh/U0m091+xGVIe72uLyLoBL4Ld/BJ9Qo8uPtEYwAXBEFj/3zqzceM6jqfytUr0H14B535Bqr593wm73dIc3JZGTKdyjXKKzkCl367rlxxyHLzyM2RYWQsoXKNCrx8+poRM/sUXZ94u1IqmIYTRZExXRaxdqX3/1wo0FD8VwPQE7HxJ/Ef6aZ1Fnvm2mM6T93PqnFt8XDSPLP/+CMrZm48qZ/VYWgiaZmG9bVnZuciiqLOAcE3NJF+7W2JvfSAp6+zaD9pL59WLsmKMW009t6rMoD9Iy6zds9Fan5cEpfWNZnmG6dU91SopeqqzSMIgkZtHtV7U9hOFuQqQD63wtTYiFuZH7Fs7VK15biiiP/gpZTUp2m8SMtWEcm7xYaI6+SIZsUGf9CfNxIff5KRy5bovD0UzrGDZmXNouoCmqDoZPFZEKJsHfWY3hO/ZXuKZRcroMj325S0wmVoYcKY6izaaWBb5KLI7OFr8T+ygBvJqcwcvIqx8wbg5Na2yHRVwVqIIu0E+Tn8PJmcoITlGBvnhyptHIH184L49dglRLnIiFl9dF4pFUzDRQbHQp6R1tXh/zf8/0hkvSPS0rN48iqT9pP2MLlPE85sHKBsa5QIAiNWHGX1+LZa880AG8Iu62916GqHmQE2j68zpEpSmC7wi0j605Rm0wDmDf0cK3Njbj96w+3HrxHFfP6B6+wwyjj7YtJhHWWcfXGdFcY3604wxTuW/h3qEJf4AFf7Wmqx9+VAAAAgAElEQVRuYvoWhAu2pGq6N1XbSVWIosiKHedZtc6H8KhoOnfurJZCUMh3LF3jR8QVI+oMDcaqiw91hgYTcUXC0jV+XL5y/S/xbE1PyzA4x66K/BbEY2p99pd+u663Y5ZD12ZqOkfN2zZUCq7pgojtJ8iVyghKWK6Vh6Cqi+88sC1yuZzOtUYytf9yRszqg/OgdnrzDRSGNeHbj7Plx/38sP1bZfDXBlEUOR5+hi86NMbU3MSgnn9RFAkPOsGG73cSdiD8/02Ovzj8O+6yGFhbmdNrbqRGYpWuAS7u0gODrA5NjSUGeQy3alhJN0OZiMtM841jUp+mKqY0DVk6sjWmJkbM3hRPOdeNzNgQr0ZYSwkahkvrmkSfvUuZEuYcu5DKm0wppazN8l3B3pLifEM1F4S1QVWbRxM02U4q4BeRhGBekuHDh2s9vkQioXPnzoRFHublqzRksjxevkojLPJwoQHjfcLaxsowU5QC7lzN2zbM9whW6bM3tIAry/1zAFAQwgKX79PJbMZv2R5W7piuVR2zYAAXRZH+Y7rSsNmnfFKzok5dOlCYbxB38BwmpsZsXLyb7sM76CTJEBUSS/rrDFJvPNZ7peQypD0BP+zN9wreeBhBEP5Rpu7vinf6axAEoY8gCMmCIMgFQdCacxIEoYsgCNcFQbghCMJ373LOvwL16tbFzFiiMcjrGuDeRaJg5Y5zejErf9p3kan9mheyeVT14vWPTKLFqBDW7rnI9AHNCUtQH2Q8nBpSrpQFMjksH+3AxQA3jWSuK1uHMHfI51y7+wIr83wWbqfm1ZSkOEMHvvjEhxrvTZPtpMJUfsH2S0RERX+QszN7h9Z6k5RiD57DxNREzbw87VUGHXp8gbfXn65ghjpmWViacfp4IrOHrcG53hiGtZtJZkY2vgt3MKTNdxpN0z07zmXTkt2sD5ujU/BVDeAOXZuRknTXoHRV6Fsv5J0bDtHDvSPeYXM4sPlosQ5ekcExbFsZgSw3j+TzNwxaKd39/SGeM3vjf2QBWZnZxe/0/wjvWgNIAnoCG7VtIAiCEeBNvmXkfeCMIAhh/wtbSG0wkgha0zdxlx4QML34fKBCokCfYq6iM+fB8wyd9fH9IpKQ5YnKvL0uvgAv0rJZtUs9OIkiZOfIWDnWocief9Ue/dl+J9kfd4MRzo3Yt9CZ9pP2GFzD0JTfV9pO/vGGnNw8mtQuh39kEhsOJHL7jzRWrl7/wc7OJoz7hsnTx+uVYw/beowe7l+TEH1B2UZpZCyhum1lxnkNJMQ7kl0bD1G5egWDHLMAfpobRP9xjmqcgbiD5wj2jsT/hz14e4WQnZWDlY0Fdi1tMTM3ZdTsvlStVamYM+RDNYAv8BtPbo5M7yCsqIVEBMfw/I+XXDn7O3UaVWPVrhnMH7FeK9dAtSe/WfOmZKZnGuwt3LJdI16/SMPaxkqv/f/peFdLyKtAcV/4lsANURRvvd12B+DK/9gYXhXJV6/hOlNzwU/Xmb0hYnH7Y29gYizhlHdfvhy7C0SK7Wuf4h3L6Y39lQXU4uSaQXPAjT57l49KWehVtF6/7yLLg8/h6dQQ2yqlOb6mN008gwwa+FTz+6qtqGNc7fjWO45sqYx6Q7blD2QjWnP3SRqh+3bh4aEfmenvQqdOnZBPlhC1I65IHoACkcExZKRlcvXcDRJPpyg7bExMjHn26CUHthylU5/WhG49xounbwjxjtJrcNnpe5Cu/dswzmtAoSKss1s7nAa2VXbClP6oBAMnOOE0oC3O9cbg0LWZXveuCOAKn16DaiFpWWxZuZ9ylcpQuWbFfLP5nFwW+I/nj/vPCd1ylA0L889RoqSNWk/+jRs3kBgJmFmYGtTqqkjDvYuZyz8Vf8daujKQqvL7/bevaYQgCCMFQTgrCMLZp0+f/uUXB/lFYG1BXlfxMdW8uC4QRRGfA4lIZXKS77ygWgUb1u4tJp2z+wJGEoHypfT7A9NUUPUNzU+16LNUH9+jMS/Ts5VG8rZVSvN1s6r6a/PE3aB1w0r59xZxmeYjQ1i07TdMjY3YeSyFc34DyT32Dc/DRxO62IXOLavR06EWcScNM9v+OyCRSAgPjWDbivBi0xYRQSfw9grB1MyU1l2aEnRyuVIbx3Nmb8pWLMWbl+mEeEcy/NsehF1Zj7Gxkc76OxFBJ8iV5jJ2vnbTHNUcvkQiEPDDXiKDY96JMBZ/6DymZiYGGrwbs2bPTPqO6sKjO0+UReYpfX6gUpVyLN4yie0nf6BESRtevXxNZPhBZZtmN1dnOvZuhYWlud5pOIU3sSiKhG+NZeL4ScXv9P8IxQ4AgiD8IghCkoYfVx3PoekbqDVKiqK4SRTF5qIoNi9XrpyOp/gThqg42lhbaA3yqh0vRUE1L64LAiKTuP34NY1qlGXljnNM6tOExMDBLPZsTXjCLeoM2opVZ2/qDNpKeMItFnu25lLgINo1+cRgMTRVGJS7d/iUzGwZU33jaDkqhDLOvkT+epvletYwVuw4R9Svd6jaJ4BvfeL4/f4rGtUoy/2naVwKHKSRI1DSypS0dP0Cy98NW1tb4mLjCQuIZ2zXxYVy7BFBJxjRaR6+3+9g4AQnNh9frLHDZtOhfB9eU1MTGn9ZFyMjo/wC7op9RASdKHZw8f1+BytCpulUK3Ec0AYLK3M8ZvRm96bDSsKYPlDMog9sOUrVTyvpHYTjDp6jmUMDqtSqiH2XpiSeTtHYJaRphq4g4I2Z1w9jEyP2+kXrrVTqOqxD/uD6L2r/VKDYb4goil+LothQw0+ojue4D1RR+f0ToHAF8D3AUBVHhea/Jri0qskKHQKcRCKwb6Fzvlxz+OViO3OmeMcxvX9zFnq2IvnOc1ztaynTOaGLXXgePhrp0Ylqs2CJRDBopaFaUFXA0KK1VJZH9QolGOXSiJSgYWQeHo9EEAiI1HHgi0rG1NgI6dGJZEaP53XUWN4cHMvmmZ0pYWWmkRsAb1cx1u/m4BUUFET16tWRSCRUr16doCDdJJ71ga2tLVeTr7FmxXquxDxiqMNsutQaSd/mU9i84gDPHr9kzNz+DJ7oUrTSp1s7Rszqowx+CsetPX7RjOripXFwGdXFC/9lexgw3knvHH7C4fMEHltE9TqVDZpFV6pajoy0TAZ941KojbUoiKJI6NZjdB/eASjcGqssMsckaZyh/7R+LU5DHDAyMmJ58FT+ePCcSD2USnNzZTy5/5xtKyP+Ve2fCvwdd3sGqC0IQg1BEEyB/kDYX3GiJQu98Pu2De2bVMHE2Ij2Targ920bliz0KnK/sRMm4xtxvdCXVi4XWbXrPNlSmU4ze9sqpRntasd3G+NpMVJ7KmdZ8FmqVbBh5qAWdGpejZzcPJ2Dsd4rDS3ib4bq6luZm6h1DJmaGBG6uBvzN5/CP6KYge+tFPPehc6FAr2mVYr6+7e0irfpgqCgIEaOHMndu3cRRZG7d+8ycuTIv2QQULSiRoYf5NXL18hkeWRlZjPlm6mUKlsSJzfdirlOA9uqtUgqrBk9Z/YmIfoCg+xn0KnmCAZ8MY1TRy7iObM3ubkyXAa11+t6FbNugFfP3ug9i97jF82DW48xNTNl46KdZGZk6yUXrTCIh8KtsYoBKmD5Po0z9Pj4k8qic7XaHzN/4zi85wcTvr3oVteI/2vvzMNruvb//1onCRFCua6fUomLxlil1G2L8uXWVKWlLUK5aqipphbVUFGiLYr2lpJEWpoTShWJxDwkhrZK1ZhK+9VQ1W8vpRqJyHDW74/kHGc++5wkMq3X8+R5cvbZZ691tlifvT7D+6M/QHjYBrzwZtunR0zibuWNgqaBPieEuAw8DsQLIXbmH68jhEgAkFLmABOAnUAysEFKqW31cpM8FUfLRSRPxfGC089169aNTENFohKSLY7vOnYRv4o+7Fz0nOac+8Xrj/PemA50fLguU5cn2nXlNKtfg8kvtEYIkSceV6mC5sXYuNOYvVrDTsPJgtuwTjWPfPedWz9g8+RqDAgv++J7+zGMbadp8tIaPth4wq4MhKNdisX7cT8wfuJUt+ZrTkhICBkZlq6NjIwMhg0bVqQ7AiM6nY4jXx3ihVe6FUib3tgxK+zTycSdW4FfFV/Wfb2IsE8n067zQ2Sm3/HYh38s8QxVa1QhKyuHeH2ips9uiz7Af6/8wYr4OXyyP4wXx/Tg1s10Vs7/3OUiHB+TSNSiL5kXOdH05G30yZvToccjpJ7/1e4TunUBXrvOD7Fy+xxiPorn353ftNkpxekP8FKHGSwPXUerVo+w4sNwzp1JLpeLPxQ8C2gzsNnO8StAL7PXCUBCQcbSgicqjpD3n2pLXAKdOj6BRDKiVzOEEKYagMYBNdi/7Hn6zd7Gyq2nGNPXUgZ6y6H/ZeXWU9zJymX6wDaEfvI1Nfx98RI6zkcPs8mQGfR2AlEzupleP9nKvQyioHrVGf9sS6Z/fJDw2NOMsZKl3nLwJ1ZuPc2dbFsxNGO/guSL11m47rhD7R5rjEVfC0Z1cDgnU0rqlpNM+jBPJdXfrwIN7q+Kj7eXQ/kHR7uUu+87F2/TwqVL9iuPc3PziqWMOwKAwYPdbx2qhcKSizDHOmhr3WJRC8an7q1r9tF3aBfWLU/I1/EXrvX+3/0CnZeOeg1rm/z2BoNkRWgMn3+8ndi1+3n2313tykVnZWVbyEAYffKj3nzBYqzK/pXIzcm1u0gbC/DMv29gozrojyw06QbZKJW+8TxLZ3xG4n5tu5SyTJlyeL05O5RR7yex/8QvZOfksv/EL4x6P4k3Z4e6/GxQUBCJB4+wLPZnHh23mcj4MxaBUuMC5ypI+0rflmRk5nAyaohpYbfG2v+uNc5gREqJfs95hvdsRtgoy/kEDf6U2au/4satTF59vhU1qvqansQj4k7T5KW1zAw/bJJZ1upKitx2hqxsg8NFGu6mpG5d0IcPJnam52P1uRb7Cmm3s1k8tqPN4i+lJCLutMNdSt4u5ixz1p5gc2x8gfyzAQGO520kIyODkJAQj8dwRWHJRZhjXSTmSdcs41P3yW/OU9m/EpWrVuI/sbPYGL7TbrwhPiYxr3I2Yicfxc6iWg1/i85hvYM7Ubf+/2PCvCG0fKwxUYu+ZEiHGXRvOJohHWZwZNcJRs58nqi98y2KzazdQUbS025Txd9+L2BHBXjmO6XJC4YS0Oh+MtJuc+XSVY4lnaVDx/Zu3aOySpkSgzOKd02aF0pyyhaaBjVg/sJlmkW9jFoyu3fvZvmHS0zSB0a05tynZ2abArazIo/YPGWbF41Zxxm07AIit53h12u3mDmkHbWq+9nMx1zJc+aqw6bisH82q83F//uLShW9+Pb877w+4BHeivoKCS47nIV+8jUHPrDtI+CIZzs0ZPJ/Epn8n0R+v5HBpatpXLt520KcbUVsMqlXrlOnVjWSTv1Kjaq+Hou3uSIsLIzRo0fbuIGscbRTKAzsPa26wp5chDnW4nF9h3Uh8t1NbtUMGJ+6v957kt2bjtB3WBcCGt5P1L75jp+iZz5P2yebo9PpGDC2J2+PWWGhud/q8SZsXbOX3y5eJetONq+EvOiwxaVxN2FUFLU29M7y810V4O3d8jWr39vEtMUv81C7Bzl99EfCJqxi5PBXXN6b8oCSg3bCfdWqkLJ2sNtSzUYZY0etE/uGxNLniQaMeLoFO46mMjvyK6JndafLlE0u202ujj/DlI+SyLiTw509r5okmbWQnZNL5e7LufLlKLYe+l8+3nqKm+lZ6EReExsb11a+rv75X27wzcqBNAv8m9tjNahTjUxDRRo1asSJkydJu5WBfxU/OrZ/gvETp9K1a1dmzZpFVMQKrt1Io7KvD1Lo6NK5E+MnTi3U5ht6vZ6QkBAuXbqETqczuX/MCQwMJDU1tVDGs+bpZ3rSrHPBJaPNObr/FBHvfkH4jrkIITAYDLzcZRYvvNJd0zjxMYlsjNhJ1N759Gk+HiTojyx0v4Vi+xlsOf0fk1rolk/2cvnn39HpBFl3cvD21vG32tUZNK6XQ3fQvEhbfX5X8swGg4GmzZvQZ2RHuwV4w7uEMHHeEFq3v9vr48ThZMJDN5N89rzm71iacEcOuky5gAobZ+mhjjDPZjFPDTUPINsTUzPGGTRp+wS3xddDFVF/vwoWTdvfCG5LWsYdxvVtaeFKCnxxNWt2JhM2sj3eBSg+S147lNnBLUn+4Qe+OXrcRpxtw4bP2RC9mnWznuL2rglsCXuGWtX9GTh4aKGLtw0ePJjU1FQMBgNr1qzBz8/yO/n5+REWFlZo41nz6vhJbIlyHBi1xjxP3RFtO7Xg5vVbpqCtueibq4K0OP0BVi/cROjKcaT9mU6dwFpkpHtYCJaeaVnLsHMu4+cMIicnF58K3oyfG8zE+UM4vPMEL7adSvcGo5y6g4y4ys93VYB36ccrXP3tOsO7hNC13nCGdwnh6m/XSfnhJ7e+Y1mlTLmACptxr07hzSmj3AuUbjnFglF3/YtB9aqzb2l/uk/bzOL1x3l9YBv6PNGA23dyTGJqRq0hi0CqC22fpRu+c1t6wjrV0lznZ+nGExZBWmMT+e7tAnmy1QMej5U3RjMkkuf6PM3pc+ctFnXz1F3AlLo7aZ7zBizuYP7kHxAQQFhYmCnQ6+h4UdCtWzeu/d8N4tcl0VtLT9voA/x14xZtOtq2KYX8TBp9Iml/3iLinY0g8lJHjTUDs0d8aFdH5+D242z+ZA9//P4n2Vk5jHjqLfyq+PJAg9r4eNg5zNpNJYSg95DOIGDF3HW0796a6jWr0a7zQ3fbW07r5zTIvC36ANFLEziYdMjpg4CxAO+Zvr3ZtvYgvYd2NH3fGrWqEbXwS2YsHWlyAb03JZLadWpp/n5lGeUCcoLBYKBF0yCm9Glgt1ewNeFxp1kQ/S1HVw6kun9FUwvFVQk/kmmowGvTZ7L1yw0cPHyEv9LS8fXxIjMr16K7lvP55HUrW7H5JLuPXyI7x0BVvwp2m81bI6Wk7eh1LBjV3iZmIKXk0VfWETby7nvmrqwdR1OZFXmEb1cN0mwIrceSUvLouM28syzCYivv5eVFxs5x+HjflR3OzsnFr/sKuy4adzHm/5v7/v38/AgPDy/Sxd4RVfyrUKGSFyOm93eZYRP53ib8KvviV8XXJpMmaftxYtfsIzs7h3mREwHuNozPX/ArVa7IkV3fs255PKkpV8jOysGvii8PP9aYvv/uavLhGzEYDPRvPYmRbzxfaG4qKSUvdZjBhLcH81jXu2m+Fg3u7Qi9bVmzl8sXfufkiVOa4z8Gg4Hdu3fz4UfLOHzoCLfS0qlYyYf5UZNsXEBLp0VzKfWy5u9YmnDHBaQMgAtSUlLo1PEJ5gx52GGTdKNvPiTyCC0a1OT7H6+antxzcg18sTnOri87JSWFhx9qzsUNL7uMM1h3K7NuSfnx1lNkZuWams1bY2xB6SgV0/jEvzWsD3DXh5+1d6LDWIYjHI0VGX+Wbed0xMbvNB1r0fRBPhjd0iJ1d/+JX5gUfoozyT+6HMsV9evX5+LFizbHi9LX7wwvLy+i9s5jzujlDhc/c5943X/U4pt9p5g37mO8vL1MgdjK/pXo0LMN494aaPq7MhgMpqDtqaMppnPrP1iH69f+Ym3SOw71/Y3ExySyfkUCaw++61b7yVFvvmC3UxdAnP4AhxKO8Z7+dYvjjubbsl0QT/V/gqUzPuPmn39pvLP28fLyYueFcLx97jo7crJz6N5gdKE8YJREVEvIQsSYHtqmVQtWxZ5mrHXOfX4NwNUbGdSo6kvSyV9pGlCDmUMe5am2ATQeFuOwt2hQUBD/6trFpXvFWc9dR83mjUbAuum7ox3Csx0aMmPlIdNrcwE5YyzjfyZ/gcEgXSqWOhrr2Q4NmBERY3HMkwbs7uAoq6cos32cUcW/MvfVrKo5wwag2SMNAUHcuRWm6xzdf4rIdzdZ/DsYUx+tF+KQfy+jx8COLhd/gJ4DO7JuRQLx+sQ8F44LHKVumvNkzzZ8PHe9zXFH84U8Q1QYqZpBTRpx+uiPFjuA00d/JKhJowJfuyygDIAGgoKC6Ny5Mw39rhJ35IKNb/6ptoFsPJDCiild7i5ii/aw/8Rll/IF4yc6jzO403N3xNMtkDLv/H1L+xN75IKpQM1ZI3awlYzecvAn/tmsNtk5udxMzyLp5GUqVfRm+spDLN14gqkDHnFYDOdoLHuCbgVN3XVFQECA3R2AlrqAosCYt/70oE4OFz9rDm3/Dh8fb+JjEk1uo7adWrBi7noS1ie5dNec/OY805dok9HW6XS8s2YKk/ovQErpsK2jq9RNcyr7VyLz9h23eljHrUnig8XLNc3ZGbND5jB92uu8tmioKQbw/rS1LFywuMDXLgsoF5BGduzYwZtTRvHtin42f8Qth0fzwcRONm6MF0MTiNmwxeEOAFzHGYxpokdXOZb2NUdKSZOX1nDlj3S6PFKPcX0fttv03Rpzn7+UkubDPuPy1VtkZuWYaggOnbrC9bgx7P3uFwa9nQACbmVkmwyhq7Gu3bxN42Ex3PgzzeX3KCxKWgxgx44dTJk+gRXbZ2n+9xzbYz7TJs9k4eL30PlIU5DzxtWbTB2wkOHT+tHbSY5914CX2f1zJF7erncARlLPX2bs029zf8Df6T+ym00QOXbtfoepm9bcvJ7Gi22nMjFsiObU1Liow5w7k1woWWAxMTHMC5tLyg8/EdSkEbND5hAcHFzg65ZUlAuoCOjWrRtT8/WCrBfq5EvX7WoQ3UjLdClf4EiGwognPXdfH9CGiG1nTP58LZhnCK1OOIuXTvBn/FjTYm6MCXh76+jeLpCOD9c11TJoH6Nggm6eUBzZPs5wt3GMMQ1y+PDhDB8+3BTkjAgL4VZaOpX8fFn19gYiFmzk1l8Z1GtYm8ETevPPLi1NXbM8yeyp/vdqePl4MeatgUS++wUfhHxGbk4uPhV8CAyqY+OmcsahHd/R4tEHiVr4JUhcBr/XLt7mMvPHHYKDg8v0gl8QVB2ARowL9VtrvyMy/qxFvnHTgBocOm2pcH3o9BVq16pJy+aNXfYmsCdDYawB8ES3/7knG3E29Q+3JaPH9m3pUEDOuqmMR7LUBRR08xTz/P/U1NRiW/zBvcYxxn63RhE0W5XRHFatDOe+v1VlbvgEdl2IYHLYUFbO/5zgx6ZzLvE3li36iK7/6uqRPMTD/2xMu84PMWJ6f2rXq8meS1G8HTkBaZA82qmFpgXaWMswYGxPln0x06G8xLZ8kba4qMPlVpmzOFAGwA0cLdTTB7Vh5MI9FhpEA+ftxFvkaO5NYJSheGdZBNvOedF4WAyVe6ywkaPQQrXKFcjKdq85zR9/ZRIScdihYqd1DYG7stSvvL+Pi7/9Qa9evTQ36imruGocEx+TyLieYZoWw3lhc3lt0TBat2+Kt483rds3ZfbysQQE1jN1zZo4YRLb1iR5XIAWu3YfA8b2NMUesu5kk7BeY3ey6AOmILG5nHXsZ/sZ0O41kz7QwYRj3Lh6q1wrcxYHKgbgAcZ84+UfLuHg4SOk3crAt6IPfr4V+OPPWzQNakh6ejqRUx4vcHqjp3IUQYM/pVJFb5fSEhHbzvDa8iTaNavN9IFt7frwHdUQGLOTXI3xyvv7iP/qAtGzethk+hRWsLc0Yi9vvYp/Zdp3eIKJEyZrksHQkuboSi7BGnN5CJ1OR++mY9EfvisP4U4h1/LQGCJ2zbOIE9hLHY2PSeRc4m/Ex213feMUTlExgCLGuBV3Ftz18vJy0Jtgi1tjGeUo3K3C7fhwXRaN6ehQwnpz0k98tPkkBoPkeESw0wwhR3LNxj4A/WbH8f7n3/GaTWZQnqBb6pVrbAztWaTVvqURLX9HrtCS5mh0O3V8sgNI6dIHb53ZYy057arS2LyQy8vbyyZIbJ06WphZPwr3KGhDmBeEEGeFEAYhhEOLI4RIFUKcFkJ8L4QoeY/0RYCxN4E5WnoTWOOoW5kjzJurBNWrzsnVg+nY8gHeWHWEgBdXmySsNxz4kV+v3uLV51vx4AP3ObyWs6YykGcEJj7/CJnSl7izd11XjYfFsO2cjneWRXDz1h2PGvUoXDM7ZA7vT1vLicPJ5GTncOJwMu9PW8vskDkW55m7nYZ2fMOpxLO5Rj/YSk6D/e5k5nLPgyf0BqDV401Mn3HUBKa89uMtCRR0B3AG6Aes0nDu/0gprxVwvFJDYRU4Ocs+ssfq+DPcyc6l9YN/JzL+rElS+etj3wPw7DO98NVlMaBLEPNHPM7L7+1m1Vb7BW6L1x8nMyuXfUv7290h5BV+nWPuZ987lWz2tFFPceBMN6gkYsxumRd6N81x4YLFdrNejP2KH3u8HbGf7XdZgGbEWnLaiLNCrm36A1Tyq0jvIZ25eT3NbhOYosr6UWinUGIAQogDwOtSSrtP90KIVKCtuwagpMYAtBITo2fBvFCSUy7QNKgBb872zOVhlKOYO7S1TZqokbyWlGeYujyJzOxcqvpXNkkum/uSreMXf6WlU8m3IjWrV+PGzTTSb2daxDOqVfalmn8lZga3tuveuSMrsjk23mngLiZGzywHxrAkuYBKWs1AUeFuLcI3+06xPDSGNYnvaK5deLnLLK5c/C9ZWdlUqOhD/aC6DBrXiye6teJ2+h1TiqrM0RG7JU4FfguRe64FpMEA/AzcACSwSkoZ7uRao4HRAAEBAW3sVXGWR1JSUkxP72N6B3m8GLvC3mL98sL91Kz9ABd+/tlGz1+rXn9hGcOipKTpBhUV7gaFt0UfYFXYRsbMzmvq4oo4/QFWvv05x7/9jqCgoAIHuhXuUagGQAixB7BX6hcipdyaf84BnBuAOlLKK0KIWsBu4FUppcs8stK+Ayhs7GUfebIYO6OoxdlKMjqdzm6sxdhopSyRkpJCxyc7MPT13poKs9au+Yyhw15i6Gu96RXsJPNHn8jHc9ez+UvnFfCKosXCUQ8AAAX8SURBVKPE7QCszg0FbkkpXYpxKANw7ylqeeaSTHnZARhJSUnhmb69LeQlzDN5rF00d8830Hvokzby1JvCdyFzvNi5fRdNmjRxPQFFkeCOAUBKWeAf4AB5Pn5771UG/M1+PwL00HLdNm3aSMW9pXmTRnLPkn4yd/8k08+eJf1k8yaNinlmRU90dLT08/OT5LkqJSD9/PxkdHR0cU+tyMjNzZU7duyQvXr3kNXuqyq9vLxktfuqyl69e8gdO3bI3NzcAp1vD71eL5s0C5I6nU42aRYk9Xp9UX29cglwTGpdu7WeaPfD8BxwGbgD/A7szD9eB0jI/70BcDL/5yx5riNN11cG4N6j10fLf9StKfcs6Sczd0+Qe5b0k/+oW1Pq9QVfBKOjo2VgYKAUQsjAwMASubCWhjmWZvR6vawbeL9csmGG3J0aKZdsmCHrBt6vjEAh4o4BUJXAChuKImBbXjJsFM5p2rwxo0OfK1dN2u81qiOYosRR3vzrCvuUxw5d9xp3DIDKv1LcE0paZy5F8WCUrjBHdegqPpQBUNwTHHXgKq7OXIriQat0heLeoAxAKUGv11O/fn10Oh3169dHry9dcsphYWH4+flZHPPz8yMsLKyYZqQoDoKDg1m4YDHhoZvp3mA04aGbHUpXKO4BWqPFxfGjsoDyKCvpiSrDRqEoelBZQGULFUBVKBRaUUHgMoYKoCoUiqJAGYBSgJYAammPESgUinuPMgClAFcBVGOR1cWLF5FScvHiRUaPHq2MgEKhcIoyAKWAwYMHEx4eTmBgIEIIAgMDLSpoQ0JCLCpsATIyMggJCSmO6SoUilKCCgKXAcqTjLFCoXCOCgKXM1SRlUKh8ARlAMoAqshKoVB4gjIAZQBXMQKFQqGwh4oBKBQKRRninsUAhBCLhBA/CCFOCSE2CyHuc3BeDyHEeSHET0KINwoypkKhUCgKh4K6gHYDLaSULYEUYKb1CUIIL2A50BNoBgwSQjQr4LgKhUKhKCAFMgBSyl1Sypz8l18DD9g5rR3wk5TygpQyC1gP9C3IuAqFQqEoOIUZBH4Z2G7neF3gF7PXl/OP2UUIMVoIcUwIcezq1auFOD2FQqFQmOPt6gQhxB6gtp23QqSUW/PPCQFyAHvaA8LOMYeRZyllOBAOeUFgV/NTKBQKhWe4NABSyn85e18IMQzoDXSV9lOKLgP1zF4/AFzRMrnjx49fE0LY6iAXDzWBa8U9iRKOukfaUPfJNeoeacPefQrU+uECpYEKIXoAS4BOUkq7/hohhDd5AeKuwK/At0CwlPKsxwMXA0KIY1pTq8or6h5pQ90n16h7pI2C3qeCxgA+AvyB3UKI74UQK/MnVUcIkQCQHySeAOwEkoENpW3xVygUirKISxeQM6SUjRwcvwL0MnudACQUZCyFQqFQFC5KCkI74cU9gVKAukfaUPfJNeoeaaNA96lES0EoFAqFouhQOwCFQqEopygDoFAoFOUUZQDcQKv4XXlGCPGCEOKsEMIghFBpfGYoUUTXCCGihBD/FUKcKe65lFSEEPWEEPuFEMn5/9cmeXotZQDcw6X4nYIzQD8gqbgnUpJQooia+RToUdyTKOHkAK9JKZsCjwHjPf1bUgbADTSK35VrpJTJUsrzxT2PEogSRdSAlDIJuF7c8yjJSCl/k1J+l/97Gnn1VQ711ZyhDIDnOBK/Uyjs4ZYookKhBSFEfaA18I0nny9QIVhZpBDE78o8Wu6Rwga3RBEVClcIIaoAm4DJUsq/PLmGMgBWFIL4XZnH1T1S2MVjUUSFwhohhA95i79eSvmlp9dRLiA3yBe/mwH0kVJmFPd8FKWKb4EHhRD/EEJUAAYCscU8J0UpRAghgNVAspRySUGupQyAe9gVv1PcRQjxnBDiMvA4EC+E2FnccyoJKFFEbQgh1gFfAY2FEJeFECOKe04lkPbAS0CX/HXoeyFEL1cfsoeSglAoFIpyitoBKBQKRTlFGQCFQqEopygDoFAoFOUUZQAUCoWinKIMgEKhUJRTlAFQKBSKcooyAAqFQlFO+f/igp0fj/IHdgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 绘图展示结果\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Black removed and is used for noise instead.\n",
    "unique_labels = set(labels)\n",
    "colors = [plt.cm.Spectral(each)\n",
    "          for each in np.linspace(0, 1, len(unique_labels))]\n",
    "for k, col in zip(unique_labels, colors):\n",
    "    if k == -1:\n",
    "        # Black used for noise.\n",
    "        col = [0, 0, 0, 1]\n",
    "\n",
    "    class_member_mask = (labels == k)\n",
    "\n",
    "    xy = X[class_member_mask & core_samples_mask]\n",
    "    plt.plot(xy[:, 0], xy[:, 1], 'o', markerfacecolor=tuple(col),\n",
    "             markeredgecolor='k', markersize=14)\n",
    "\n",
    "    xy = X[class_member_mask & ~core_samples_mask]\n",
    "    plt.plot(xy[:, 0], xy[:, 1], 'o', markerfacecolor=tuple(col),\n",
    "             markeredgecolor='k', markersize=6)\n",
    "\n",
    "plt.title('Estimated number of clusters: %d' % n_clusters_)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
